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500 | 500 | ['Ji Young Lee', 'Franck Dernoncourt', 'Ozlem Uzuner', 'Peter Szolovits'] | 1610.09704v1 | Patient notes contain a wealth of information of potentially great interest
to medical investigators. However, to protect patients' privacy, Protected
Health Information (PHI) must be removed from the patient notes before they can
be legally released, a process known as patient note de-identification. The
main objective for a de-identification system is to have the highest possible
recall. Recently, the first neural-network-based de-identification system has
been proposed, yielding state-of-the-art results. Unlike other systems, it does
not rely on human-engineered features, which allows it to be quickly deployed,
but does not leverage knowledge from human experts or from electronic health
records (EHRs). In this work, we explore a method to incorporate
human-engineered features as well as features derived from EHRs to a
neural-network-based de-identification system. Our results show that the
addition of features, especially the EHR-derived features, further improves the
state-of-the-art in patient note de-identification, including for some of the
most sensitive PHI types such as patient names. Since in a real-life setting
patient notes typically come with EHRs, we recommend developers of
de-identification systems to leverage the information EHRs contain. | Feature-Augmented Neural Networks for Patient Note De-identification | 2,016 | http://arxiv.org/pdf/1610.09704v1 | Title FeatureAugmented Neural Networks Patient Note Deidentification Summary Patient note contain wealth information potentially great interest medical investigator However protect patient privacy Protected Health Information PHI must removed patient note legally released process known patient note deidentification main objective deidentification system highest possible recall Recently first neuralnetworkbased deidentification system proposed yielding stateoftheart result Unlike system rely humanengineered feature allows quickly deployed leverage knowledge human expert electronic health record EHRs work explore method incorporate humanengineered feature well feature derived EHRs neuralnetworkbased deidentification system result show addition feature especially EHRderived feature improves stateoftheart patient note deidentification including sensitive PHI type patient name Since reallife setting patient note typically come EHRs recommend developer deidentification system leverage information EHRs contain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.043702758848667145, 0.08156780898571014, -0.015921566635370255, -0.013011055067181587, -0.005612940061837435, 0.007986437529325485, 0.04966949298977852, 0.019367963075637817, 0.006645694375038147, 0.005578699987381697, 0.03567885980010033, -0.015435667708516121, 0.021586939692497253, 0.08970491588115692, 0.01117122732102871, 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501 | 501 | ['Kartik Audhkhasi', 'Bhuvana Ramabhadran', 'George Saon', 'Michael Picheny', 'David Nahamoo'] | 1703.07754v1 | Recent work on end-to-end automatic speech recognition (ASR) has shown that
the connectionist temporal classification (CTC) loss can be used to convert
acoustics to phone or character sequences. Such systems are used with a
dictionary and separately-trained Language Model (LM) to produce word
sequences. However, they are not truly end-to-end in the sense of mapping
acoustics directly to words without an intermediate phone representation. In
this paper, we present the first results employing direct acoustics-to-word CTC
models on two well-known public benchmark tasks: Switchboard and CallHome.
These models do not require an LM or even a decoder at run-time and hence
recognize speech with minimal complexity. However, due to the large number of
word output units, CTC word models require orders of magnitude more data to
train reliably compared to traditional systems. We present some techniques to
mitigate this issue. Our CTC word model achieves a word error rate of
13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or
decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also
present rescoring results on CTC word model lattices to quantify the
performance benefits of a LM, and contrast the performance of word and phone
CTC models. | Direct Acoustics-to-Word Models for English Conversational Speech
Recognition | 2,017 | http://arxiv.org/pdf/1703.07754v1 | Title Direct AcousticstoWord Models English Conversational Speech Recognition Summary Recent work endtoend automatic speech recognition ASR shown connectionist temporal classification CTC loss used convert acoustic phone character sequence system used dictionary separatelytrained Language Model LM produce word sequence However truly endtoend sense mapping acoustic directly word without intermediate phone representation paper present first result employing direct acousticstoword CTC model two wellknown public benchmark task Switchboard CallHome model require LM even decoder runtime hence recognize speech minimal complexity However due large number word output unit CTC word model require order magnitude data train reliably compared traditional system present technique mitigate issue CTC word model achieves word error rate 130188 Hub52000 SwitchboardCallHome test set without LM decoder compared 96160 phonebased CTC 4gram LM also present rescoring result CTC word model lattice quantify performance benefit LM contrast performance word phone CTC model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02484346553683281, 0.033995211124420166, 0.01255255751311779, 0.0641455352306366, -0.03553905338048935, -0.00043097714660689235, 0.03510015085339546, 0.015079144388437271, -0.02754133567214012, -0.04431154206395149, -0.0484241284430027, -0.020511770620942116, 0.047198012471199036, 0.029331816360354424, -0.006576798856258392, -0.01237565092742443, 0.0036125709302723408, -0.01736290007829666, 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502 | 502 | ['Oleksii Kuchaiev', 'Boris Ginsburg'] | 1703.10722v3 | We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and states into the independent groups. Both approaches allow us to
train large LSTM networks significantly faster to the near state-of the art
perplexity while using significantly less RNN parameters. | Factorization tricks for LSTM networks | 2,017 | http://arxiv.org/pdf/1703.10722v3 | Title Factorization trick LSTM network Summary present two simple way reducing number parameter accelerating training large Long ShortTerm Memory LSTM network first one matrix factorization design LSTM matrix product two smaller matrix second one partitioning LSTM matrix input state independent group approach allow u train large LSTM network significantly faster near stateof art perplexity using significantly le RNN parameter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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503 | 503 | ['Franck Dernoncourt', 'Ji Young Lee', 'Peter Szolovits'] | 1705.05487v1 | Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone. | NeuroNER: an easy-to-use program for named-entity recognition based on
neural networks | 2,017 | http://arxiv.org/pdf/1705.05487v1 | Title NeuroNER easytouse program namedentity recognition based neural network Summary Namedentity recognition NER aim identifying entity interest text Artificial neural network ANNs recently shown outperform existing NER system However ANNs remain challenging use nonexpert user paper present NeuroNER easytouse namedentity recognition tool based ANNs Users annotate entity using graphical webbased user interface BRAT annotation used train ANN turn predict entity location category new text NeuroNER make annotationtrainingprediction flow smooth accessible anyone Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England 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504 | 504 | ['Zhenisbek Assylbekov', 'Rustem Takhanov', 'Bagdat Myrzakhmetov', 'Jonathan N. Washington'] | 1707.06480v1 | Syllabification does not seem to improve word-level RNN language modeling
quality when compared to character-based segmentation. However, our best
syllable-aware language model, achieving performance comparable to the
competitive character-aware model, has 18%-33% fewer parameters and is trained
1.2-2.2 times faster. | Syllable-aware Neural Language Models: A Failure to Beat Character-aware
Ones | 2,017 | http://arxiv.org/pdf/1707.06480v1 | Title Syllableaware Neural Language Models Failure Beat Characteraware Ones Summary Syllabification seem improve wordlevel RNN language modeling quality compared characterbased segmentation However best syllableaware language model achieving performance comparable competitive characteraware model 1833 fewer parameter trained 1222 time faster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.017384110018610954, 0.08129464089870453, 0.02031603455543518, 0.02979023940861225, -0.022303177043795586, -0.01334854494780302, 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505 | 505 | ['Iñigo Casanueva', 'Paweł Budzianowski', 'Pei-Hao Su', 'Nikola Mrkšić', 'Tsung-Hsien Wen', 'Stefan Ultes', 'Lina Rojas-Barahona', 'Steve Young', 'Milica Gašić'] | 1711.11023v1 | Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid
the significant effort needed to hand-craft the required dialogue flow, the
Dialogue Management (DM) module can be cast as a continuous Markov Decision
Process (MDP) and trained through Reinforcement Learning (RL). Several RL
models have been investigated over recent years. However, the lack of a common
benchmarking framework makes it difficult to perform a fair comparison between
different models and their capability to generalise to different environments.
Therefore, this paper proposes a set of challenging simulated environments for
dialogue model development and evaluation. To provide some baselines, we
investigate a number of representative parametric algorithms, namely deep
reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and
compare them to a non-parametric model, GP-SARSA. Both the environments and
policy models are implemented using the publicly available PyDial toolkit and
released on-line, in order to establish a testbed framework for further
experiments and to facilitate experimental reproducibility. | A Benchmarking Environment for Reinforcement Learning Based Task
Oriented Dialogue Management | 2,017 | http://arxiv.org/pdf/1711.11023v1 | Title Benchmarking Environment Reinforcement Learning Based Task Oriented Dialogue Management Summary Dialogue assistant rapidly becoming indispensable daily aid avoid significant effort needed handcraft required dialogue flow Dialogue Management DM module cast continuous Markov Decision Process MDP trained Reinforcement Learning RL Several RL model investigated recent year However lack common benchmarking framework make difficult perform fair comparison different model capability generalise different environment Therefore paper proposes set challenging simulated environment dialogue model development evaluation provide baseline investigate number representative parametric algorithm namely deep reinforcement learning algorithm DQN A2C Natural ActorCritic compare nonparametric model GPSARSA environment policy model implemented using publicly available PyDial toolkit released online order establish testbed framework experiment facilitate experimental reproducibility Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07061508297920227, 0.004467794205993414, -0.0014938859967514873, 0.006259709130972624, -0.043233588337898254, 0.005580393597483635, 0.024448717013001442, -0.03563292324542999, -0.012285783886909485, -0.01634962111711502, -0.03158413618803024, 0.0056253825314342976, -0.03055431693792343, 0.0707760900259018, -0.04185229912400246, -0.08981728553771973, 0.03431781008839607, -0.019321244210004807, 0.04103340208530426, 0.034855060279369354, 0.012249049730598927, 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506 | 506 | ['Zhenisbek Assylbekov', 'Rustem Takhanov'] | 1802.08375v1 | We propose several ways of reusing subword embeddings and other weights in
subword-aware neural language models. The proposed techniques do not benefit a
competitive character-aware model, but some of them improve the performance of
syllable- and morpheme-aware models while showing significant reductions in
model sizes. We discover a simple hands-on principle: in a multi-layer input
embedding model, layers should be tied consecutively bottom-up if reused at
output. Our best morpheme-aware model with properly reused weights beats the
competitive word-level model by a large margin across multiple languages and
has 20%-87% fewer parameters. | Reusing Weights in Subword-aware Neural Language Models | 2,018 | http://arxiv.org/pdf/1802.08375v1 | Title Reusing Weights Subwordaware Neural Language Models Summary propose several way reusing subword embeddings weight subwordaware neural language model proposed technique benefit competitive characteraware model improve performance syllable morphemeaware model showing significant reduction model size discover simple handson principle multilayer input embedding model layer tied consecutively bottomup reused output best morphemeaware model properly reused weight beat competitive wordlevel model large margin across multiple language 2087 fewer parameter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao 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507 | 507 | ['Isabelle Augenstein', 'Sebastian Ruder', 'Anders Søgaard'] | 1802.09913v1 | We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
aspect- and topic-based sentiment analysis. | Multi-task Learning of Pairwise Sequence Classification Tasks Over
Disparate Label Spaces | 2,018 | http://arxiv.org/pdf/1802.09913v1 | Title Multitask Learning Pairwise Sequence Classification Tasks Disparate Label Spaces Summary combine multitask learning semisupervised learning inducing joint embedding space disparate label space learning transfer function label embeddings enabling u jointly leverage unlabelled data auxiliary annotated datasets evaluate approach variety sequence classification task disparate label space outperform strong single multitask baseline achieve new stateoftheart aspect topicbased sentiment analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author 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508 | 508 | ['Caiming Xiong', 'Stephen Merity', 'Richard Socher'] | 1603.01417v1 | Neural network architectures with memory and attention mechanisms exhibit
certain reasoning capabilities required for question answering. One such
architecture, the dynamic memory network (DMN), obtained high accuracy on a
variety of language tasks. However, it was not shown whether the architecture
achieves strong results for question answering when supporting facts are not
marked during training or whether it could be applied to other modalities such
as images. Based on an analysis of the DMN, we propose several improvements to
its memory and input modules. Together with these changes we introduce a novel
input module for images in order to be able to answer visual questions. Our new
DMN+ model improves the state of the art on both the Visual Question Answering
dataset and the \babi-10k text question-answering dataset without supporting
fact supervision. | Dynamic Memory Networks for Visual and Textual Question Answering | 2,016 | http://arxiv.org/pdf/1603.01417v1 | Title Dynamic Memory Networks Visual Textual Question Answering Summary Neural network architecture memory attention mechanism exhibit certain reasoning capability required question answering One architecture dynamic memory network DMN obtained high accuracy variety language task However shown whether architecture achieves strong result question answering supporting fact marked training whether could applied modality image Based analysis DMN propose several improvement memory input module Together change introduce novel input module image order able answer visual question new DMN model improves state art Visual Question Answering dataset babi10k text questionanswering dataset without supporting fact supervision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05473654344677925, -0.014790971763432026, -0.048675354570150375, 0.033531010150909424, 0.00014234644186217338, 0.024418624117970467, -0.011225576512515545, -0.001926077646203339, 0.0046080113388597965, -0.028277456760406494, 0.02012714184820652, -0.0230622086673975, -0.016158970072865486, 0.049191515892744064, 0.051195766776800156, -0.09142211079597473, 0.04454417899250984, -0.016408348456025124, 0.006697871722280979, 0.004929772578179836, 0.055175088346004486, -0.007436016108840704, -0.012218624353408813, 0.0004362610634416342, -0.033957548439502716, -0.03501845896244049, -0.045663971453905106, -0.008086206391453743, 0.015784891322255135, -0.02626478113234043, 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509 | 509 | ['Fabio Carrara', 'Andrea Esuli', 'Tiziano Fagni', 'Fabrizio Falchi', 'Alejandro Moreo Fernández'] | 1606.07287v1 | In this paper we tackle the problem of image search when the query is a short
textual description of the image the user is looking for. We choose to
implement the actual search process as a similarity search in a visual feature
space, by learning to translate a textual query into a visual representation.
Searching in the visual feature space has the advantage that any update to the
translation model does not require to reprocess the, typically huge, image
collection on which the search is performed. We propose Text2Vis, a neural
network that generates a visual representation, in the visual feature space of
the fc6-fc7 layers of ImageNet, from a short descriptive text. Text2Vis
optimizes two loss functions, using a stochastic loss-selection method. A
visual-focused loss is aimed at learning the actual text-to-visual feature
mapping, while a text-focused loss is aimed at modeling the higher-level
semantic concepts expressed in language and countering the overfit on
non-relevant visual components of the visual loss. We report preliminary
results on the MS-COCO dataset. | Picture It In Your Mind: Generating High Level Visual Representations
From Textual Descriptions | 2,016 | http://arxiv.org/pdf/1606.07287v1 | Title Picture Mind Generating High Level Visual Representations Textual Descriptions Summary paper tackle problem image search query short textual description image user looking choose implement actual search process similarity search visual feature space learning translate textual query visual representation Searching visual feature space advantage update translation model require reprocess typically huge image collection search performed propose Text2Vis neural network generates visual representation visual feature space fc6fc7 layer ImageNet short descriptive text Text2Vis optimizes two loss function using stochastic lossselection method visualfocused loss aimed learning actual texttovisual feature mapping textfocused loss aimed modeling higherlevel semantic concept expressed language countering overfit nonrelevant visual component visual loss report preliminary result MSCOCO dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03907022997736931, 0.039165105670690536, 0.004019522573798895, 0.06499049067497253, -0.020583219826221466, 0.005293701775372028, -0.0029849207494407892, 0.021723872050642967, -0.015071997418999672, -0.053929056972265244, -0.03275226429104805, 0.00982444453984499, -0.003892771666869521, 0.06876673549413681, 0.016681667417287827, -0.11507431417703629, 0.00662872102111578, 0.02946915477514267, -0.02720458433032036, 0.023457810282707214, 0.04196619242429733, 0.02471049688756466, 0.03806030750274658, 0.04753061756491661, -0.028528593480587006, 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510 | 510 | ['Marc Tanti', 'Albert Gatt', 'Kenneth P. Camilleri'] | 1703.09137v2 | When a recurrent neural network language model is used for caption
generation, the image information can be fed to the neural network either by
directly incorporating it in the RNN -- conditioning the language model by
`injecting' image features -- or in a layer following the RNN -- conditioning
the language model by `merging' image features. While both options are attested
in the literature, there is as yet no systematic comparison between the two. In
this paper we empirically show that it is not especially detrimental to
performance whether one architecture is used or another. The merge architecture
does have practical advantages, as conditioning by merging allows the RNN's
hidden state vector to shrink in size by up to four times. Our results suggest
that the visual and linguistic modalities for caption generation need not be
jointly encoded by the RNN as that yields large, memory-intensive models with
few tangible advantages in performance; rather, the multimodal integration
should be delayed to a subsequent stage. | Where to put the Image in an Image Caption Generator | 2,017 | http://arxiv.org/pdf/1703.09137v2 | Title put Image Image Caption Generator Summary recurrent neural network language model used caption generation image information fed neural network either directly incorporating RNN conditioning language model injecting image feature layer following RNN conditioning language model merging image feature option attested literature yet systematic comparison two paper empirically show especially detrimental performance whether one architecture used another merge architecture practical advantage conditioning merging allows RNNs hidden state vector shrink size four time result suggest visual linguistic modality caption generation need jointly encoded RNN yield large memoryintensive model tangible advantage performance rather multimodal integration delayed subsequent stage Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03209516778588295, 0.055982545018196106, -0.006379342172294855, 0.030374499037861824, -0.03485017269849777, -0.0006257386994548142, 0.035780780017375946, -0.0061155143193900585, -0.026076918467879295, -0.04781739413738251, -0.006454643327742815, -0.02906055562198162, 0.05437486618757248, 0.07614684104919434, 0.033357735723257065, -0.1054292619228363, 0.0026980889961123466, 0.012665991671383381, -0.05012086033821106, 0.01622002199292183, 0.022476010024547577, -0.009181022644042969, 0.028786834329366684, 0.031797830015420914, -0.022571945562958717, 0.02714105322957039, -0.023349428549408913, 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511 | 511 | ['Ilija Ilievski', 'Shuicheng Yan', 'Jiashi Feng'] | 1604.01485v1 | Visual Question and Answering (VQA) problems are attracting increasing
interest from multiple research disciplines. Solving VQA problems requires
techniques from both computer vision for understanding the visual contents of a
presented image or video, as well as the ones from natural language processing
for understanding semantics of the question and generating the answers.
Regarding visual content modeling, most of existing VQA methods adopt the
strategy of extracting global features from the image or video, which
inevitably fails in capturing fine-grained information such as spatial
configuration of multiple objects. Extracting features from auto-generated
regions -- as some region-based image recognition methods do -- cannot
essentially address this problem and may introduce some overwhelming irrelevant
features with the question. In this work, we propose a novel Focused Dynamic
Attention (FDA) model to provide better aligned image content representation
with proposed questions. Being aware of the key words in the question, FDA
employs off-the-shelf object detector to identify important regions and fuse
the information from the regions and global features via an LSTM unit. Such
question-driven representations are then combined with question representation
and fed into a reasoning unit for generating the answers. Extensive evaluation
on a large-scale benchmark dataset, VQA, clearly demonstrate the superior
performance of FDA over well-established baselines. | A Focused Dynamic Attention Model for Visual Question Answering | 2,016 | http://arxiv.org/pdf/1604.01485v1 | Title Focused Dynamic Attention Model Visual Question Answering Summary Visual Question Answering VQA problem attracting increasing interest multiple research discipline Solving VQA problem requires technique computer vision understanding visual content presented image video well one natural language processing understanding semantics question generating answer Regarding visual content modeling existing VQA method adopt strategy extracting global feature image video inevitably fails capturing finegrained information spatial configuration multiple object Extracting feature autogenerated region regionbased image recognition method cannot essentially address problem may introduce overwhelming irrelevant feature question work propose novel Focused Dynamic Attention FDA model provide better aligned image content representation proposed question aware key word question FDA employ offtheshelf object detector identify important region fuse information region global feature via LSTM unit questiondriven representation combined question representation fed reasoning unit generating answer Extensive evaluation largescale benchmark dataset VQA clearly demonstrate superior performance FDA wellestablished baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06563566625118256, -0.00966651365160942, -0.027689313516020775, 0.04710132256150246, -0.010935337282717228, 0.02032623253762722, -0.0013326873304322362, 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512 | 512 | ['Remi Lebret', 'Pedro O. Pinheiro', 'Ronan Collobert'] | 1412.8419v3 | Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
on the recently release Microsoft COCO dataset. | Simple Image Description Generator via a Linear Phrase-Based Approach | 2,014 | http://arxiv.org/pdf/1412.8419v3 | Title Simple Image Description Generator via Linear PhraseBased Approach Summary Generating novel textual description image interesting problem connects computer vision natural language processing paper present simple model able generate descriptive sentence given sample image model strong focus syntax description train purely bilinear model learns metric image representation generated previously trained Convolutional Neural Network phrase used described system able infer phrase given image sample Based caption syntax statistic propose simple language model produce relevant description given test image using phrase inferred approach considerably simpler stateoftheart model achieves comparable result recently release Microsoft COCO dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04166076332330704, 0.03131520375609398, 0.03395695239305496, 0.07672151923179626, -0.032109953463077545, 0.0065893144346773624, 0.007731662131845951, 0.014008475467562675, -0.014690528623759747, -0.08661362528800964, 0.016673613339662552, 0.01575004868209362, 0.006975891999900341, 0.06346464902162552, 0.04067310690879822, -0.11925859749317169, -0.015114739537239075, 0.0032656688708812, 0.00673786411061883, 0.004076729062944651, 0.021006671711802483, 0.01640670746564865, 0.015808820724487305, 0.023812687024474144, -0.04428974166512489, -0.017753226682543755, -0.001337240799330175, 0.01792103238403797, 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513 | 513 | ['Lin Ma', 'Zhengdong Lu', 'Lifeng Shang', 'Hang Li'] | 1504.06063v5 | In this paper, we propose multimodal convolutional neural networks (m-CNNs)
for matching image and sentence. Our m-CNN provides an end-to-end framework
with convolutional architectures to exploit image representation, word
composition, and the matching relations between the two modalities. More
specifically, it consists of one image CNN encoding the image content, and one
matching CNN learning the joint representation of image and sentence. The
matching CNN composes words to different semantic fragments and learns the
inter-modal relations between image and the composed fragments at different
levels, thus fully exploit the matching relations between image and sentence.
Experimental results on benchmark databases of bidirectional image and sentence
retrieval demonstrate that the proposed m-CNNs can effectively capture the
information necessary for image and sentence matching. Specifically, our
proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and
Microsoft COCO databases achieve the state-of-the-art performances. | Multimodal Convolutional Neural Networks for Matching Image and Sentence | 2,015 | http://arxiv.org/pdf/1504.06063v5 | Title Multimodal Convolutional Neural Networks Matching Image Sentence Summary paper propose multimodal convolutional neural network mCNNs matching image sentence mCNN provides endtoend framework convolutional architecture exploit image representation word composition matching relation two modality specifically consists one image CNN encoding image content one matching CNN learning joint representation image sentence matching CNN composes word different semantic fragment learns intermodal relation image composed fragment different level thus fully exploit matching relation image sentence Experimental result benchmark database bidirectional image sentence retrieval demonstrate proposed mCNNs effectively capture information necessary image sentence matching Specifically proposed mCNNs bidirectional image sentence retrieval Flickr30K Microsoft COCO database achieve stateoftheart performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04177653044462204, 0.06474702805280685, -0.0027690697461366653, 0.08906528353691101, -0.05987587571144104, -0.008205855265259743, 0.002720879390835762, 0.026942765340209007, -0.05217777565121651, -0.05570228770375252, -0.0026668068021535873, 0.007719388231635094, -0.03881571441888809, 0.04712129756808281, 0.0068499986082315445, -0.06033490598201752, 0.04456336051225662, 0.046650294214487076, -0.04368356615304947, 0.023909971117973328, 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514 | 514 | ['Jacob Andreas', 'Marcus Rohrbach', 'Trevor Darrell', 'Dan Klein'] | 1601.01705v4 | We describe a question answering model that applies to both images and
structured knowledge bases. The model uses natural language strings to
automatically assemble neural networks from a collection of composable modules.
Parameters for these modules are learned jointly with network-assembly
parameters via reinforcement learning, with only (world, question, answer)
triples as supervision. Our approach, which we term a dynamic neural model
network, achieves state-of-the-art results on benchmark datasets in both visual
and structured domains. | Learning to Compose Neural Networks for Question Answering | 2,016 | http://arxiv.org/pdf/1601.01705v4 | Title Learning Compose Neural Networks Question Answering Summary describe question answering model applies image structured knowledge base model us natural language string automatically assemble neural network collection composable module Parameters module learned jointly networkassembly parameter via reinforcement learning world question answer triple supervision approach term dynamic neural model network achieves stateoftheart result benchmark datasets visual structured domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 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515 | 515 | ['Taehwan Kim', 'Weiran Wang', 'Hao Tang', 'Karen Livescu'] | 1602.04278v1 | We study the problem of recognition of fingerspelled letter sequences in
American Sign Language in a signer-independent setting. Fingerspelled sequences
are both challenging and important to recognize, as they are used for many
content words such as proper nouns and technical terms. Previous work has shown
that it is possible to achieve almost 90% accuracies on fingerspelling
recognition in a signer-dependent setting. However, the more realistic
signer-independent setting presents challenges due to significant variations
among signers, coupled with the dearth of available training data. We
investigate this problem with approaches inspired by automatic speech
recognition. We start with the best-performing approaches from prior work,
based on tandem models and segmental conditional random fields (SCRFs), with
features based on deep neural network (DNN) classifiers of letters and
phonological features. Using DNN adaptation, we find that it is possible to
bridge a large part of the gap between signer-dependent and signer-independent
performance. Using only about 115 transcribed words for adaptation from the
target signer, we obtain letter accuracies of up to 82.7% with frame-level
adaptation labels and 69.7% with only word labels. | Signer-independent Fingerspelling Recognition with Deep Neural Network
Adaptation | 2,016 | http://arxiv.org/pdf/1602.04278v1 | Title Signerindependent Fingerspelling Recognition Deep Neural Network Adaptation Summary study problem recognition fingerspelled letter sequence American Sign Language signerindependent setting Fingerspelled sequence challenging important recognize used many content word proper noun technical term Previous work shown possible achieve almost 90 accuracy fingerspelling recognition signerdependent setting However realistic signerindependent setting present challenge due significant variation among signer coupled dearth available training data investigate problem approach inspired automatic speech recognition start bestperforming approach prior work based tandem model segmental conditional random field SCRFs feature based deep neural network DNN classifier letter phonological feature Using DNN adaptation find possible bridge large part gap signerdependent signerindependent performance Using 115 transcribed word adaptation target signer obtain letter accuracy 827 framelevel adaptation label 697 word label Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.005663355812430382, 0.042473867535591125, 0.011422141455113888, 0.00483107753098011, -0.011012137867510319, 0.011493192985653877, 0.051666442304849625, -0.010203853249549866, 0.014243020676076412, -0.012097612023353577, -0.023107748478651047, -0.008776439353823662, 0.061435308307409286, 0.051392752677202225, 0.0012588253011927009, -0.05398855730891228, 0.005206093657761812, -0.014118989929556847, -0.030642885714769363, -0.03806179389357567, 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516 | 516 | ['Armand Vilalta', 'Dario Garcia-Gasulla', 'Ferran Parés', 'Eduard Ayguadé', 'Jesus Labarta', 'Ulises Cortés', 'Toyotaro Suzumura'] | 1707.09872v2 | The current state-of-the-art for image annotation and image retrieval tasks
is obtained through deep neural networks, which combine an image representation
and a text representation into a shared embedding space. In this paper we
evaluate the impact of using the Full-Network embedding in this setting,
replacing the original image representation in a competitive multimodal
embedding generation scheme. Unlike the one-layer image embeddings typically
used by most approaches, the Full-Network embedding provides a multi-scale
representation of images, which results in richer characterizations. To measure
the influence of the Full-Network embedding, we evaluate its performance on
three different datasets, and compare the results with the original multimodal
embedding generation scheme when using a one-layer image embedding, and with
the rest of the state-of-the-art. Results for image annotation and image
retrieval tasks indicate that the Full-Network embedding is consistently
superior to the one-layer embedding. These results motivate the integration of
the Full-Network embedding on any multimodal embedding generation scheme,
something feasible thanks to the flexibility of the approach. | Full-Network Embedding in a Multimodal Embedding Pipeline | 2,017 | http://arxiv.org/pdf/1707.09872v2 | Title FullNetwork Embedding Multimodal Embedding Pipeline Summary current stateoftheart image annotation image retrieval task obtained deep neural network combine image representation text representation shared embedding space paper evaluate impact using FullNetwork embedding setting replacing original image representation competitive multimodal embedding generation scheme Unlike onelayer image embeddings typically used approach FullNetwork embedding provides multiscale representation image result richer characterization measure influence FullNetwork embedding evaluate performance three different datasets compare result original multimodal embedding generation scheme using onelayer image embedding rest stateoftheart Results image annotation image retrieval task indicate FullNetwork embedding consistently superior onelayer embedding result motivate integration FullNetwork embedding multimodal embedding generation scheme something feasible thanks flexibility approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012527558021247387, 0.01966680958867073, -0.009844220243394375, 0.037345778197050095, -0.009412907995283604, 0.007965855300426483, 0.04769733548164368, 0.0024188843090087175, 0.0004167724691797048, -0.0063164448365569115, -0.008858357556164265, -0.02805659919977188, -0.03822551667690277, 0.03703112527728081, 0.04509298875927925, -0.07267549633979797, -0.017106279730796814, 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517 | 517 | ['Marc Tanti', 'Albert Gatt', 'Kenneth P. Camilleri'] | 1708.02043v2 | In neural image captioning systems, a recurrent neural network (RNN) is
typically viewed as the primary `generation' component. This view suggests that
the image features should be `injected' into the RNN. This is in fact the
dominant view in the literature. Alternatively, the RNN can instead be viewed
as only encoding the previously generated words. This view suggests that the
RNN should only be used to encode linguistic features and that only the final
representation should be `merged' with the image features at a later stage.
This paper compares these two architectures. We find that, in general, late
merging outperforms injection, suggesting that RNNs are better viewed as
encoders, rather than generators. | What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption
Generator? | 2,017 | http://arxiv.org/pdf/1708.02043v2 | Title Role Recurrent Neural Networks RNNs Image Caption Generator Summary neural image captioning system recurrent neural network RNN typically viewed primary generation component view suggests image feature injected RNN fact dominant view literature Alternatively RNN instead viewed encoding previously generated word view suggests RNN used encode linguistic feature final representation merged image feature later stage paper compare two architecture find general late merging outperforms injection suggesting RNNs better viewed encoders rather generator Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 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518 | 518 | ['Shiliang Zhang', 'Hui Jiang', 'Mingbin Xu', 'Junfeng Hou', 'Lirong Dai'] | 1505.01504v2 | In this paper, we propose the new fixed-size ordinally-forgetting encoding
(FOFE) method, which can almost uniquely encode any variable-length sequence of
words into a fixed-size representation. FOFE can model the word order in a
sequence using a simple ordinally-forgetting mechanism according to the
positions of words. In this work, we have applied FOFE to feedforward neural
network language models (FNN-LMs). Experimental results have shown that without
using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform
not only the standard fixed-input FNN-LMs but also the popular RNN-LMs. | A Fixed-Size Encoding Method for Variable-Length Sequences with its
Application to Neural Network Language Models | 2,015 | http://arxiv.org/pdf/1505.01504v2 | Title FixedSize Encoding Method VariableLength Sequences Application Neural Network Language Models Summary paper propose new fixedsize ordinallyforgetting encoding FOFE method almost uniquely encode variablelength sequence word fixedsize representation FOFE model word order sequence using simple ordinallyforgetting mechanism according position word work applied FOFE feedforward neural network language model FNNLMs Experimental result shown without using recurrent feedback FOFE based FNNLMs significantly outperform standard fixedinput FNNLMs also popular RNNLMs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.020751405507326126, 0.08787958323955536, -0.0008128008921630681, 0.01903299055993557, -0.02275308594107628, -0.0176555123180151, 0.008528110571205616, 0.015340976417064667, -0.04496227949857712, -0.02266302891075611, 0.03872222453355789, -0.06334277987480164, 0.0518856905400753, 0.02693445049226284, 0.042597364634275436, -0.045802321285009384, 0.02974054589867592, -0.009539715014398098, -4.815565625904128e-05, 0.02373417839407921, 0.005766068585216999, -0.022920867428183556, 0.009289862588047981, 0.03118176758289337, 0.006721076555550098, -0.024276474490761757, -0.046768561005592346, -0.01778564229607582, -0.024597784504294395, -0.04283194988965988, 0.0049766371957957745, 0.044458117336034775, 0.043616168200969696, 0.04149157553911209, 2.0585594029398635e-06, 0.02030058577656746, -0.03600484877824783, -0.03977644816040993, 0.029684675857424736, 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519 | 519 | ['Chris Dyer', 'Miguel Ballesteros', 'Wang Ling', 'Austin Matthews', 'Noah A. Smith'] | 1505.08075v1 | We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed to or popped from the top of the stack in constant time, but, in
addition, an LSTM maintains a continuous space embedding of the stack contents.
This lets us formulate an efficient parsing model that captures three facets of
a parser's state: (i) unbounded look-ahead into the buffer of incoming words,
(ii) the complete history of actions taken by the parser, and (iii) the
complete contents of the stack of partially built tree fragments, including
their internal structures. Standard backpropagation techniques are used for
training and yield state-of-the-art parsing performance. | Transition-Based Dependency Parsing with Stack Long Short-Term Memory | 2,015 | http://arxiv.org/pdf/1505.08075v1 | Title TransitionBased Dependency Parsing Stack Long ShortTerm Memory Summary propose technique learning representation parser state transitionbased dependency parser primary innovation new control structure sequencetosequence neural networksthe stack LSTM Like conventional stack data structure used transitionbased parsing element pushed popped top stack constant time addition LSTM maintains continuous space embedding stack content let u formulate efficient parsing model capture three facet parser state unbounded lookahead buffer incoming word ii complete history action taken parser iii complete content stack partially built tree fragment including internal structure Standard backpropagation technique used training yield stateoftheart parsing performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.046386729925870895, 0.04305907338857651, -0.0023133051581680775, 0.036666374653577805, -0.03569711372256279, -0.025922877714037895, -0.02053554356098175, -0.004047680180519819, -0.011525302194058895, -0.0806916207075119, 0.03160813823342323, 0.01739772967994213, -0.00100998068228364, 0.08142969012260437, -0.028868261724710464, -0.07988784462213516, 0.057165395468473434, 0.03420966491103172, 0.029520902782678604, 0.06662977486848831, 0.04570317640900612, 0.028804108500480652, 0.008213764987885952, 0.033872973173856735, -0.008854170329868793, -0.04496476426720619, -0.03612801805138588, 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520 | 520 | ['Ehud Ben-Reuven', 'Jacob Goldberger'] | 1604.00317v1 | In this study we address the problem of training a neuralnetwork for language
identification using both labeled and unlabeled speech samples in the form of
i-vectors. We propose a neural network architecture that can also handle
out-of-set languages. We utilize a modified version of the recently proposed
Ladder Network semisupervised training procedure that optimizes the
reconstruction costs of a stack of denoising autoencoders. We show that this
approach can be successfully applied to the case where the training dataset is
composed of both labeled and unlabeled acoustic data. The results show enhanced
language identification on the NIST 2015 language identification dataset. | A Semisupervised Approach for Language Identification based on Ladder
Networks | 2,016 | http://arxiv.org/pdf/1604.00317v1 | Title Semisupervised Approach Language Identification based Ladder Networks Summary study address problem training neuralnetwork language identification using labeled unlabeled speech sample form ivectors propose neural network architecture also handle outofset language utilize modified version recently proposed Ladder Network semisupervised training procedure optimizes reconstruction cost stack denoising autoencoders show approach successfully applied case training dataset composed labeled unlabeled acoustic data result show enhanced language identification NIST 2015 language identification dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 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521 | 521 | ['Awni Y. Hannun', 'Andrew L. Maas', 'Daniel Jurafsky', 'Andrew Y. Ng'] | 1408.2873v2 | We present a method to perform first-pass large vocabulary continuous speech
recognition using only a neural network and language model. Deep neural network
acoustic models are now commonplace in HMM-based speech recognition systems,
but building such systems is a complex, domain-specific task. Recent work
demonstrated the feasibility of discarding the HMM sequence modeling framework
by directly predicting transcript text from audio. This paper extends this
approach in two ways. First, we demonstrate that a straightforward recurrent
neural network architecture can achieve a high level of accuracy. Second, we
propose and evaluate a modified prefix-search decoding algorithm. This approach
to decoding enables first-pass speech recognition with a language model,
completely unaided by the cumbersome infrastructure of HMM-based systems.
Experiments on the Wall Street Journal corpus demonstrate fairly competitive
word error rates, and the importance of bi-directional network recurrence. | First-Pass Large Vocabulary Continuous Speech Recognition using
Bi-Directional Recurrent DNNs | 2,014 | http://arxiv.org/pdf/1408.2873v2 | Title FirstPass Large Vocabulary Continuous Speech Recognition using BiDirectional Recurrent DNNs Summary present method perform firstpass large vocabulary continuous speech recognition using neural network language model Deep neural network acoustic model commonplace HMMbased speech recognition system building system complex domainspecific task Recent work demonstrated feasibility discarding HMM sequence modeling framework directly predicting transcript text audio paper extends approach two way First demonstrate straightforward recurrent neural network architecture achieve high level accuracy Second propose evaluate modified prefixsearch decoding algorithm approach decoding enables firstpass speech recognition language model completely unaided cumbersome infrastructure HMMbased system Experiments Wall Street Journal corpus demonstrate fairly competitive word error rate importance bidirectional network recurrence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03477975353598595, 0.03933965042233467, 0.013139639981091022, 0.0683111846446991, -0.027946796268224716, -0.005735600367188454, 0.019245104864239693, 0.008267185650765896, -0.013944591395556927, -0.0594002865254879, -0.0265803225338459, -0.041861556470394135, 0.01546946819871664, 0.056132446974515915, -0.018566139042377472, -0.01572158932685852, 0.02518036775290966, -0.040718723088502884, -0.0178676825016737, 0.006036057602614164, 0.013261918909847736, -0.004003641661256552, -0.0467354916036129, 0.04439127817749977, 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522 | 522 | ['Jose Antonio Miñarro-Giménez', 'Oscar Marín-Alonso', 'Matthias Samwald'] | 1502.03682v1 | BACKGROUND: The amount of biomedical literature is rapidly growing and it is
becoming increasingly difficult to keep manually curated knowledge bases and
ontologies up-to-date. In this study we applied the word2vec deep learning
toolkit to medical corpora to test its potential for identifying relationships
from unstructured text. We evaluated the efficiency of word2vec in identifying
properties of pharmaceuticals based on mid-sized, unstructured medical text
corpora available on the web. Properties included relationships to diseases
('may treat') or physiological processes ('has physiological effect'). We
compared the relationships identified by word2vec with manually curated
information from the National Drug File - Reference Terminology (NDF-RT)
ontology as a gold standard. RESULTS: Our results revealed a maximum accuracy
of 49.28% which suggests a limited ability of word2vec to capture linguistic
regularities on the collected medical corpora compared with other published
results. We were able to document the influence of different parameter settings
on result accuracy and found and unexpected trade-off between ranking quality
and accuracy. Pre-processing corpora to reduce syntactic variability proved to
be a good strategy for increasing the utility of the trained vector models.
CONCLUSIONS: Word2vec is a very efficient implementation for computing vector
representations and for its ability to identify relationships in textual data
without any prior domain knowledge. We found that the ranking and retrieved
results generated by word2vec were not of sufficient quality for automatic
population of knowledge bases and ontologies, but could serve as a starting
point for further manual curation. | Applying deep learning techniques on medical corpora from the World Wide
Web: a prototypical system and evaluation | 2,015 | http://arxiv.org/pdf/1502.03682v1 | Title Applying deep learning technique medical corpus World Wide Web prototypical system evaluation Summary BACKGROUND amount biomedical literature rapidly growing becoming increasingly difficult keep manually curated knowledge base ontology uptodate study applied word2vec deep learning toolkit medical corpus test potential identifying relationship unstructured text evaluated efficiency word2vec identifying property pharmaceutical based midsized unstructured medical text corpus available web Properties included relationship disease may treat physiological process physiological effect compared relationship identified word2vec manually curated information National Drug File Reference Terminology NDFRT ontology gold standard RESULTS result revealed maximum accuracy 4928 suggests limited ability word2vec capture linguistic regularity collected medical corpus compared published result able document influence different parameter setting result accuracy found unexpected tradeoff ranking quality accuracy Preprocessing corpus reduce syntactic variability proved good strategy increasing utility trained vector model CONCLUSIONS Word2vec efficient implementation computing vector representation ability identify relationship textual data without prior domain knowledge found ranking retrieved result generated word2vec sufficient quality automatic population knowledge base ontology could serve starting point manual curation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | 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523 | 523 | ['Mingxuan Wang', 'Zhengdong Lu', 'Hang Li', 'Qun Liu'] | 1503.02427v6 | Many tasks in natural language processing, ranging from machine translation
to question answering, can be reduced to the problem of matching two sentences
or more generally two short texts. We propose a new approach to the problem,
called Deep Match Tree (DeepMatch$_{tree}$), under a general setting. The
approach consists of two components, 1) a mining algorithm to discover patterns
for matching two short-texts, defined in the product space of dependency trees,
and 2) a deep neural network for matching short texts using the mined patterns,
as well as a learning algorithm to build the network having a sparse structure.
We test our algorithm on the problem of matching a tweet and a response in
social media, a hard matching problem proposed in [Wang et al., 2013], and show
that DeepMatch$_{tree}$ can outperform a number of competitor models including
one without using dependency trees and one based on word-embedding, all with
large margins | Syntax-based Deep Matching of Short Texts | 2,015 | http://arxiv.org/pdf/1503.02427v6 | Title Syntaxbased Deep Matching Short Texts Summary Many task natural language processing ranging machine translation question answering reduced problem matching two sentence generally two short text propose new approach problem called Deep Match Tree DeepMatchtree general setting approach consists two component 1 mining algorithm discover pattern matching two shorttexts defined product space dependency tree 2 deep neural network matching short text using mined pattern well learning algorithm build network sparse structure test algorithm problem matching tweet response social medium hard matching problem proposed Wang et al 2013 show DeepMatchtree outperform number competitor model including one without using dependency tree one based wordembedding large margin Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.051162995398044586, 0.018305283039808273, -0.01855611428618431, 0.07697848230600357, -0.08832608908414841, -0.0020218202844262123, -0.034851547330617905, 0.02787567302584648, 0.000578019767999649, -0.05271303653717041, 0.005947253666818142, -0.01473609171807766, 0.016358161345124245, 0.0718144103884697, -0.0034724161960184574, -0.03135335072875023, 0.0017334545264020562, 0.014667871408164501, -0.014151097275316715, 0.028176583349704742, 0.07830075919628143, 0.04513724893331528, 0.031752824783325195, 0.03252076730132103, -0.0033775358460843563, -0.008635672740638256, -0.009143606759607792, -0.011667894199490547, 0.0030632971320301294, 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524 | 524 | ['Grégoire Mesnil', 'Tomas Mikolov', "Marc'Aurelio Ranzato", 'Yoshua Bengio'] | 1412.5335v7 | Sentiment analysis is a common task in natural language processing that aims
to detect polarity of a text document (typically a consumer review). In the
simplest settings, we discriminate only between positive and negative
sentiment, turning the task into a standard binary classification problem. We
compare several ma- chine learning approaches to this problem, and combine them
to achieve the best possible results. We show how to use for this task the
standard generative lan- guage models, which are slightly complementary to the
state of the art techniques. We achieve strong results on a well-known dataset
of IMDB movie reviews. Our results are easily reproducible, as we publish also
the code needed to repeat the experiments. This should simplify further advance
of the state of the art, as other researchers can combine their techniques with
ours with little effort. | Ensemble of Generative and Discriminative Techniques for Sentiment
Analysis of Movie Reviews | 2,014 | http://arxiv.org/pdf/1412.5335v7 | Title Ensemble Generative Discriminative Techniques Sentiment Analysis Movie Reviews Summary Sentiment analysis common task natural language processing aim detect polarity text document typically consumer review simplest setting discriminate positive negative sentiment turning task standard binary classification problem compare several chine learning approach problem combine achieve best possible result show use task standard generative lan guage model slightly complementary state art technique achieve strong result wellknown dataset IMDB movie review result easily reproducible publish also code needed repeat experiment simplify advance state art researcher combine technique little effort Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.040673088282346725, 0.06416871398687363, -0.0010957929771393538, 0.022742290049791336, -0.02854306995868683, 0.02009245753288269, 0.01908349059522152, 0.005958822555840015, -0.006910823751240969, -0.06278390437364578, -0.00593555485829711, -0.03153858333826065, 0.014895033091306686, 0.03587951511144638, -0.027388690039515495, -0.036222703754901886, -0.01066835317760706, 0.04906124994158745, 0.018866941332817078, -0.016749870032072067, -0.007477681152522564, 0.03743140771985054, -0.00399798434227705, 0.007542313542217016, -0.000934806652367115, 0.003907427191734314, 0.022743506357073784, -0.01085163839161396, 0.034294530749320984, -0.03930067643523216, 0.05258022993803024, 0.05588914453983307, -0.03590165823698044, 0.003077959641814232, 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525 | 525 | ['Kartik Audhkhasi', 'Abhinav Sethy', 'Bhuvana Ramabhadran'] | 1412.7063v5 | We propose Diverse Embedding Neural Network (DENN), a novel architecture for
language models (LMs). A DENNLM projects the input word history vector onto
multiple diverse low-dimensional sub-spaces instead of a single
higher-dimensional sub-space as in conventional feed-forward neural network
LMs. We encourage these sub-spaces to be diverse during network training
through an augmented loss function. Our language modeling experiments on the
Penn Treebank data set show the performance benefit of using a DENNLM. | Diverse Embedding Neural Network Language Models | 2,014 | http://arxiv.org/pdf/1412.7063v5 | Title Diverse Embedding Neural Network Language Models Summary propose Diverse Embedding Neural Network DENN novel architecture language model LMs DENNLM project input word history vector onto multiple diverse lowdimensional subspace instead single higherdimensional subspace conventional feedforward neural network LMs encourage subspace diverse network training augmented loss function language modeling experiment Penn Treebank data set show performance benefit using DENNLM Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang 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526 | 526 | ['Dimitri Palaz', 'Mathew Magimai Doss', 'Ronan Collobert'] | 1412.7110v6 | Automatic speech recognition systems usually rely on spectral-based features,
such as MFCC of PLP. These features are extracted based on prior knowledge such
as, speech perception or/and speech production. Recently, convolutional neural
networks have been shown to be able to estimate phoneme conditional
probabilities in a completely data-driven manner, i.e. using directly temporal
raw speech signal as input. This system was shown to yield similar or better
performance than HMM/ANN based system on phoneme recognition task and on large
scale continuous speech recognition task, using less parameters. Motivated by
these studies, we investigate the use of simple linear classifier in the
CNN-based framework. Thus, the network learns linearly separable features from
raw speech. We show that such system yields similar or better performance than
MLP based system using cepstral-based features as input. | Learning linearly separable features for speech recognition using
convolutional neural networks | 2,014 | http://arxiv.org/pdf/1412.7110v6 | Title Learning linearly separable feature speech recognition using convolutional neural network Summary Automatic speech recognition system usually rely spectralbased feature MFCC PLP feature extracted based prior knowledge speech perception orand speech production Recently convolutional neural network shown able estimate phoneme conditional probability completely datadriven manner ie using directly temporal raw speech signal input system shown yield similar better performance HMMANN based system phoneme recognition task large scale continuous speech recognition task using le parameter Motivated study investigate use simple linear classifier CNNbased framework Thus network learns linearly separable feature raw speech show system yield similar better performance MLP based system using cepstralbased feature input Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.00563886109739542, 0.029790548607707024, 0.021793151274323463, 0.07984942197799683, 0.009085021913051605, -0.007258158177137375, 0.05035756155848503, 0.01637815125286579, 0.012337390333414078, -0.014996013604104519, -0.040835052728652954, 0.02297447808086872, -0.0020683889742940664, 0.07877500355243683, 0.0013255557278171182, -0.04048967361450195, -0.025622453540563583, 0.007769032847136259, 0.045553773641586304, -0.006752833724021912, 0.011163122020661831, -0.00930993165820837, -0.07498600333929062, 0.0036536995321512222, -0.04894639179110527, 0.01506821159273386, -0.0030287152621895075, -0.033405158668756485, 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527 | 527 | ['Edward Grefenstette', 'Karl Moritz Hermann', 'Mustafa Suleyman', 'Phil Blunsom'] | 1506.02516v3 | Recently, strong results have been demonstrated by Deep Recurrent Neural
Networks on natural language transduction problems. In this paper we explore
the representational power of these models using synthetic grammars designed to
exhibit phenomena similar to those found in real transduction problems such as
machine translation. These experiments lead us to propose new memory-based
recurrent networks that implement continuously differentiable analogues of
traditional data structures such as Stacks, Queues, and DeQues. We show that
these architectures exhibit superior generalisation performance to Deep RNNs
and are often able to learn the underlying generating algorithms in our
transduction experiments. | Learning to Transduce with Unbounded Memory | 2,015 | http://arxiv.org/pdf/1506.02516v3 | Title Learning Transduce Unbounded Memory Summary Recently strong result demonstrated Deep Recurrent Neural Networks natural language transduction problem paper explore representational power model using synthetic grammar designed exhibit phenomenon similar found real transduction problem machine translation experiment lead u propose new memorybased recurrent network implement continuously differentiable analogue traditional data structure Stacks Queues DeQues show architecture exhibit superior generalisation performance Deep RNNs often able learn underlying generating algorithm transduction experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.020350556820631027, 0.008127058856189251, -0.015597475692629814, 0.05192788690328598, -0.00637725880369544, 0.00024154184211511165, -0.026283787563443184, -0.016088377684354782, -0.04758871719241142, -0.03196248784661293, 0.002205190947279334, -0.05149921402335167, -0.00998506136238575, 0.036861661821603775, 0.02149498090147972, -0.04454407840967178, 0.03925006836652756, -0.016338109970092773, -0.01477368175983429, 0.03336205706000328, -0.003271399298682809, -0.030451159924268723, -0.009234953671693802, -0.0032821246422827244, -0.01110049244016409, -0.0002405249688308686, 0.006425058003515005, -0.026039086282253265, -0.021497193723917007, -0.026619164273142815, -0.017838479951024055, -0.011979233473539352, 0.02883055992424488, 0.0804453119635582, 2.3214493012346793e-06, -0.015640107914805412, 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528 | 528 | ['ShiLiang Zhang', 'Hui Jiang', 'Si Wei', 'LiRong Dai'] | 1510.02693v1 | We introduce a new structure for memory neural networks, called feedforward
sequential memory networks (FSMN), which can learn long-term dependency without
using recurrent feedback. The proposed FSMN is a standard feedforward neural
networks equipped with learnable sequential memory blocks in the hidden layers.
In this work, we have applied FSMN to several language modeling (LM) tasks.
Experimental results have shown that the memory blocks in FSMN can learn
effective representations of long history. Experiments have shown that FSMN
based language models can significantly outperform not only feedforward neural
network (FNN) based LMs but also the popular recurrent neural network (RNN)
LMs. | Feedforward Sequential Memory Neural Networks without Recurrent Feedback | 2,015 | http://arxiv.org/pdf/1510.02693v1 | Title Feedforward Sequential Memory Neural Networks without Recurrent Feedback Summary introduce new structure memory neural network called feedforward sequential memory network FSMN learn longterm dependency without using recurrent feedback proposed FSMN standard feedforward neural network equipped learnable sequential memory block hidden layer work applied FSMN several language modeling LM task Experimental result shown memory block FSMN learn effective representation long history Experiments shown FSMN based language model significantly outperform feedforward neural network FNN based LMs also popular recurrent neural network RNN LMs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0170398261398077, 0.02921907790005207, 0.0004469997074920684, 0.053692590445280075, -0.015155096538364887, -0.004730031359940767, 0.001771857962012291, -0.0001782434555934742, 0.039970770478248596, -0.026382112875580788, -0.013815529644489288, -0.04723210632801056, 0.022966090589761734, 0.041136160492897034, 0.004244022071361542, -0.024207627400755882, 0.03848345950245857, 0.010002311319112778, -0.008748376742005348, 0.018365250900387764, 0.015601281076669693, -0.021270612254738808, -0.012206951156258583, -0.007986417971551418, -0.030020443722605705, -0.010499735362827778, -0.024008717387914658, -0.032398026436567307, -0.026153231039643288, -0.06280133128166199, 0.03179974481463432, 0.016381939873099327, 0.022241761907935143, 0.03781496360898018, 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529 | 529 | ['Yi-Hsiu Liao', 'Hung-yi Lee', 'Lin-shan Lee'] | 1511.02506v1 | In this paper we propose the Structured Deep Neural Network (structured DNN)
as a structured and deep learning framework. This approach can learn to find
the best structured object (such as a label sequence) given a structured input
(such as a vector sequence) by globally considering the mapping relationships
between the structures rather than item by item.
When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learn utterance by utterance as a whole, rather than frame by
frame.
Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning approach. This approach was
shown to beat structured SVM in preliminary experiments on TIMIT. | Towards Structured Deep Neural Network for Automatic Speech Recognition | 2,015 | http://arxiv.org/pdf/1511.02506v1 | Title Towards Structured Deep Neural Network Automatic Speech Recognition Summary paper propose Structured Deep Neural Network structured DNN structured deep learning framework approach learn find best structured object label sequence given structured input vector sequence globally considering mapping relationship structure rather item item automatic speech recognition viewed special case structured learning problem acoustic vector sequence input phoneme label sequence output becomes possible comprehensively learn utterance utterance whole rather frame frame Structured Support Vector Machine structured SVM proposed perform ASR structured learning previously limited linear nature SVM propose structured DNN use nonlinear transformation multilayers structured deep learning approach approach shown beat structured SVM preliminary experiment TIMIT Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.020786505192518234, 0.031145649030804634, 0.023787403479218483, 0.03606473654508591, 0.007402504328638315, -0.011239790357649326, 0.030399184674024582, -0.007328341715037823, 0.006598798092454672, -0.012059276923537254, -0.017939789220690727, -0.0021720367949455976, 0.022716106846928596, 0.07752376049757004, -0.00674624927341938, -0.10935638099908829, -0.013388141989707947, -0.032961852848529816, 0.042399726808071136, 0.004155914299190044, -0.009219324216246605, 0.01203496940433979, 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530 | 530 | ['Kyuyeon Hwang', 'Wonyong Sung'] | 1601.06581v2 | In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-character unidirectional recurrent neural network (RNN), which is
end-to-end trained with connectionist temporal classification (CTC), and an
RNN-based character-level language model (LM). The output values of the
CTC-trained RNN are character-level probabilities, which are processed by beam
search decoding. The RNN LM augments the decoding by providing long-term
dependency information. We propose tree-based online beam search with
additional depth-pruning, which enables the system to process infinitely long
input speech with low latency. This system not only responds quickly on speech
but also can dictate out-of-vocabulary (OOV) words according to pronunciation.
The proposed model achieves the word error rate (WER) of 8.90% on the Wall
Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284
training set. | Character-Level Incremental Speech Recognition with Recurrent Neural
Networks | 2,016 | http://arxiv.org/pdf/1601.06581v2 | Title CharacterLevel Incremental Speech Recognition Recurrent Neural Networks Summary realtime speech recognition application latency important issue developed characterlevel incremental speech recognition ISR system responds quickly even speech hypothesis gradually improved speaking proceeds algorithm employ speechtocharacter unidirectional recurrent neural network RNN endtoend trained connectionist temporal classification CTC RNNbased characterlevel language model LM output value CTCtrained RNN characterlevel probability processed beam search decoding RNN LM augments decoding providing longterm dependency information propose treebased online beam search additional depthpruning enables system process infinitely long input speech low latency system responds quickly speech also dictate outofvocabulary OOV word according pronunciation proposed model achieves word error rate WER 890 Wall Street Journal WSJ Nov92 20K evaluation set trained WSJ SI284 training set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0044223531149327755, 0.05571771040558815, 0.03744976967573166, 0.038522932678461075, -0.008043334819376469, -0.006230231840163469, 0.005893508903682232, -0.0026893592439591885, -0.051229141652584076, -0.003056955523788929, -0.018325883895158768, -0.04175221920013428, 0.06111201271414757, 0.07964888960123062, -0.004247619304805994, -0.06234579533338547, 0.022543542087078094, -0.020144464448094368, 0.022437889128923416, 0.0021793602500110865, 0.03139384463429451, 0.010923693887889385, 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531 | 531 | ['Daniel Andor', 'Chris Alberti', 'David Weiss', 'Aliaksei Severyn', 'Alessandro Presta', 'Kuzman Ganchev', 'Slav Petrov', 'Michael Collins'] | 1603.06042v2 | We introduce a globally normalized transition-based neural network model that
achieves state-of-the-art part-of-speech tagging, dependency parsing and
sentence compression results. Our model is a simple feed-forward neural network
that operates on a task-specific transition system, yet achieves comparable or
better accuracies than recurrent models. We discuss the importance of global as
opposed to local normalization: a key insight is that the label bias problem
implies that globally normalized models can be strictly more expressive than
locally normalized models. | Globally Normalized Transition-Based Neural Networks | 2,016 | http://arxiv.org/pdf/1603.06042v2 | Title Globally Normalized TransitionBased Neural Networks Summary introduce globally normalized transitionbased neural network model achieves stateoftheart partofspeech tagging dependency parsing sentence compression result model simple feedforward neural network operates taskspecific transition system yet achieves comparable better accuracy recurrent model discus importance global opposed local normalization key insight label bias problem implies globally normalized model strictly expressive locally normalized model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan 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532 | 532 | ['Peng Li', 'Heng Huang'] | 1603.09381v1 | We report an implementation of a clinical information extraction tool that
leverages deep neural network to annotate event spans and their attributes from
raw clinical notes and pathology reports. Our approach uses context words and
their part-of-speech tags and shape information as features. Then we hire
temporal (1D) convolutional neural network to learn hidden feature
representations. Finally, we use Multilayer Perceptron (MLP) to predict event
spans. The empirical evaluation demonstrates that our approach significantly
outperforms baselines. | Clinical Information Extraction via Convolutional Neural Network | 2,016 | http://arxiv.org/pdf/1603.09381v1 | Title Clinical Information Extraction via Convolutional Neural Network Summary report implementation clinical information extraction tool leverage deep neural network annotate event span attribute raw clinical note pathology report approach us context word partofspeech tag shape information feature hire temporal 1D convolutional neural network learn hidden feature representation Finally use Multilayer Perceptron MLP predict event span empirical evaluation demonstrates approach significantly outperforms baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 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533 | 533 | ['David Krueger', 'Tegan Maharaj', 'János Kramár', 'Mohammad Pezeshki', 'Nicolas Ballas', 'Nan Rosemary Ke', 'Anirudh Goyal', 'Yoshua Bengio', 'Aaron Courville', 'Chris Pal'] | 1606.01305v4 | We propose zoneout, a novel method for regularizing RNNs. At each timestep,
zoneout stochastically forces some hidden units to maintain their previous
values. Like dropout, zoneout uses random noise to train a pseudo-ensemble,
improving generalization. But by preserving instead of dropping hidden units,
gradient information and state information are more readily propagated through
time, as in feedforward stochastic depth networks. We perform an empirical
investigation of various RNN regularizers, and find that zoneout gives
significant performance improvements across tasks. We achieve competitive
results with relatively simple models in character- and word-level language
modelling on the Penn Treebank and Text8 datasets, and combining with recurrent
batch normalization yields state-of-the-art results on permuted sequential
MNIST. | Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations | 2,016 | http://arxiv.org/pdf/1606.01305v4 | Title Zoneout Regularizing RNNs Randomly Preserving Hidden Activations Summary propose zoneout novel method regularizing RNNs timestep zoneout stochastically force hidden unit maintain previous value Like dropout zoneout us random noise train pseudoensemble improving generalization preserving instead dropping hidden unit gradient information state information readily propagated time feedforward stochastic depth network perform empirical investigation various RNN regularizers find zoneout give significant performance improvement across task achieve competitive result relatively simple model character wordlevel language modelling Penn Treebank Text8 datasets combining recurrent batch normalization yield stateoftheart result permuted sequential MNIST Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.019840899854898453, 0.05786997452378273, 0.011844219639897346, 0.013934220187366009, -0.03447861969470978, -0.015363059937953949, 0.04581072926521301, 0.005135738290846348, -0.029061265289783478, 0.005191651172935963, -0.04723426699638367, -0.0017743544885888696, 0.02767947129905224, 0.02805199846625328, 0.005984567571431398, -0.08909592032432556, 0.03620849922299385, -0.0044659338891506195, -0.025435658171772957, -0.0052337972447276115, 0.03908579796552658, -0.010324309580028057, 0.002675491152331233, 0.06141616031527519, -0.006467283237725496, 0.012957789935171604, -0.019458850845694542, 0.023581096902489662, 0.02990426868200302, 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534 | 534 | ['Isabelle Augenstein', 'Tim Rocktäschel', 'Andreas Vlachos', 'Kalina Bontcheva'] | 1606.05464v2 | Stance detection is the task of classifying the attitude expressed in a text
towards a target such as Hillary Clinton to be "positive", negative" or
"neutral". Previous work has assumed that either the target is mentioned in the
text or that training data for every target is given. This paper considers the
more challenging version of this task, where targets are not always mentioned
and no training data is available for the test targets. We experiment with
conditional LSTM encoding, which builds a representation of the tweet that is
dependent on the target, and demonstrate that it outperforms encoding the tweet
and the target independently. Performance is improved further when the
conditional model is augmented with bidirectional encoding. We evaluate our
approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving
performance second best only to a system trained on semi-automatically labelled
tweets for the test target. When such weak supervision is added, our approach
achieves state-of-the-art results. | Stance Detection with Bidirectional Conditional Encoding | 2,016 | http://arxiv.org/pdf/1606.05464v2 | Title Stance Detection Bidirectional Conditional Encoding Summary Stance detection task classifying attitude expressed text towards target Hillary Clinton positive negative neutral Previous work assumed either target mentioned text training data every target given paper considers challenging version task target always mentioned training data available test target experiment conditional LSTM encoding build representation tweet dependent target demonstrate outperforms encoding tweet target independently Performance improved conditional model augmented bidirectional encoding evaluate approach SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best system trained semiautomatically labelled tweet test target weak supervision added approach achieves stateoftheart result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.024833083152770996, 0.0354040190577507, 0.015504857525229454, 0.05491749569773674, -0.017649494111537933, 0.036230795085430145, -0.014465161599218845, 0.01846902258694172, -0.0023728450760245323, -0.08561266958713531, -0.0020045368000864983, -0.037640057504177094, -0.049539320170879364, 0.03160585090517998, -0.014274907298386097, -0.016337135806679726, 0.038536738604307175, 0.08352740854024887, 0.03255182504653931, -0.014901012182235718, 0.07084184139966965, 0.051862962543964386, -0.025173332542181015, 0.008653536438941956, -0.007838557474315166, -0.019297750666737556, 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535 | 535 | ['Noura Al Moubayed', 'Toby Breckon', 'Peter Matthews', 'A. Stephen McGough'] | 1606.05554v1 | In This paper we present a novel approach to spam filtering and demonstrate
its applicability with respect to SMS messages. Our approach requires minimum
features engineering and a small set of la- belled data samples. Features are
extracted using topic modelling based on latent Dirichlet allocation, and then
a comprehensive data model is created using a Stacked Denoising Autoencoder
(SDA). Topic modelling summarises the data providing ease of use and high
interpretability by visualising the topics using word clouds. Given that the
SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA
is able to model the messages and accurately discriminate between the two
classes without the need for a pre-labelled training set. The results are
compared against the state-of-the-art spam detection algorithms with our
proposed approach achieving over 97% accuracy which compares favourably to the
best reported algorithms presented in the literature. | SMS Spam Filtering using Probabilistic Topic Modelling and Stacked
Denoising Autoencoder | 2,016 | http://arxiv.org/pdf/1606.05554v1 | Title SMS Spam Filtering using Probabilistic Topic Modelling Stacked Denoising Autoencoder Summary paper present novel approach spam filtering demonstrate applicability respect SMS message approach requires minimum feature engineering small set la belled data sample Features extracted using topic modelling based latent Dirichlet allocation comprehensive data model created using Stacked Denoising Autoencoder SDA Topic modelling summarises data providing ease use high interpretability visualising topic using word cloud Given SMS message regarded either spam unwanted ham wanted SDA able model message accurately discriminate two class without need prelabelled training set result compared stateoftheart spam detection algorithm proposed approach achieving 97 accuracy compare favourably best reported algorithm presented literature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07865723967552185, -0.014085316099226475, -0.03809920698404312, 0.018198154866695404, -0.04089958220720291, 0.0033303394448012114, 0.027079053223133087, -0.013853617012500763, -0.0011168353958055377, -0.06289788335561752, 0.018436282873153687, 0.0021972579415887594, -0.010859817266464233, 0.07428963482379913, -0.05543696507811546, -0.023683695122599602, 0.0018998101586475968, -0.010612805373966694, 0.04359638690948486, 0.01699233241379261, -3.984752402175218e-05, -0.0021076160483062267, 0.019931837916374207, 0.008773925714194775, -0.06276939064264297, 0.019018469378352165, 0.013173902407288551, 0.005517180077731609, 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536 | 536 | ['Abhyuday Jagannatha', 'Hong Yu'] | 1606.07953v2 | Sequence labeling for extraction of medical events and their attributes from
unstructured text in Electronic Health Record (EHR) notes is a key step towards
semantic understanding of EHRs. It has important applications in health
informatics including pharmacovigilance and drug surveillance. The state of the
art supervised machine learning models in this domain are based on Conditional
Random Fields (CRFs) with features calculated from fixed context windows. In
this application, we explored various recurrent neural network frameworks and
show that they significantly outperformed the CRF models. | Bidirectional Recurrent Neural Networks for Medical Event Detection in
Electronic Health Records | 2,016 | http://arxiv.org/pdf/1606.07953v2 | Title Bidirectional Recurrent Neural Networks Medical Event Detection Electronic Health Records Summary Sequence labeling extraction medical event attribute unstructured text Electronic Health Record EHR note key step towards semantic understanding EHRs important application health informatics including pharmacovigilance drug surveillance state art supervised machine learning model domain based Conditional Random Fields CRFs feature calculated fixed context window application explored various recurrent neural network framework show significantly outperformed CRF model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan 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537 | 537 | ['Liang Lu'] | 1607.01963v5 | Highway deep neural network (HDNN) is a type of depth-gated feedforward
neural network, which has shown to be easier to train with more hidden layers
and also generalise better compared to conventional plain deep neural networks
(DNNs). Previously, we investigated a structured HDNN architecture for speech
recognition, in which the two gate functions were tied across all the hidden
layers, and we were able to train a much smaller model without sacrificing the
recognition accuracy. In this paper, we carry on the study of this architecture
with sequence-discriminative training criterion and speaker adaptation
techniques on the AMI meeting speech recognition corpus. We show that these two
techniques improve speech recognition accuracy on top of the model trained with
the cross entropy criterion. Furthermore, we demonstrate that the two gate
functions that are tied across all the hidden layers are able to control the
information flow over the whole network, and we can achieve considerable
improvements by only updating these gate functions in both sequence training
and adaptation experiments. | Sequence Training and Adaptation of Highway Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1607.01963v5 | Title Sequence Training Adaptation Highway Deep Neural Networks Summary Highway deep neural network HDNN type depthgated feedforward neural network shown easier train hidden layer also generalise better compared conventional plain deep neural network DNNs Previously investigated structured HDNN architecture speech recognition two gate function tied across hidden layer able train much smaller model without sacrificing recognition accuracy paper carry study architecture sequencediscriminative training criterion speaker adaptation technique AMI meeting speech recognition corpus show two technique improve speech recognition accuracy top model trained cross entropy criterion Furthermore demonstrate two gate function tied across hidden layer able control information flow whole network achieve considerable improvement updating gate function sequence training adaptation experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.008032679557800293, 0.06332380324602127, -0.0070065963082015514, 0.0451037734746933, 0.0027463044971227646, 0.0019146653357893229, 0.04481321945786476, 0.0075764842331409454, 0.0069970544427633286, -0.04749356210231781, -0.031650710850954056, -0.0027913234662264585, 0.024920636788010597, 0.032488442957401276, 0.006819265428930521, -0.10691617429256439, 0.02166345715522766, -0.0052026500925421715, 0.006947152782231569, -0.010900378227233887, 0.0029016772750765085, -0.006544159725308418, 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538 | 538 | ['Julian Georg Zilly', 'Rupesh Kumar Srivastava', 'Jan Koutník', 'Jürgen Schmidhuber'] | 1607.03474v5 | Many sequential processing tasks require complex nonlinear transition
functions from one step to the next. However, recurrent neural networks with
'deep' transition functions remain difficult to train, even when using Long
Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of
recurrent networks based on Gersgorin's circle theorem that illuminates several
modeling and optimization issues and improves our understanding of the LSTM
cell. Based on this analysis we propose Recurrent Highway Networks, which
extend the LSTM architecture to allow step-to-step transition depths larger
than one. Several language modeling experiments demonstrate that the proposed
architecture results in powerful and efficient models. On the Penn Treebank
corpus, solely increasing the transition depth from 1 to 10 improves word-level
perplexity from 90.6 to 65.4 using the same number of parameters. On the larger
Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform
all previous results and achieve an entropy of 1.27 bits per character. | Recurrent Highway Networks | 2,016 | http://arxiv.org/pdf/1607.03474v5 | Title Recurrent Highway Networks Summary Many sequential processing task require complex nonlinear transition function one step next However recurrent neural network deep transition function remain difficult train even using Long ShortTerm Memory LSTM network introduce novel theoretical analysis recurrent network based Gersgorins circle theorem illuminates several modeling optimization issue improves understanding LSTM cell Based analysis propose Recurrent Highway Networks extend LSTM architecture allow steptostep transition depth larger one Several language modeling experiment demonstrate proposed architecture result powerful efficient model Penn Treebank corpus solely increasing transition depth 1 10 improves wordlevel perplexity 906 654 using number parameter larger Wikipedia datasets character prediction text8 enwik8 RHNs outperform previous result achieve entropy 127 bit per character Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009730732999742031, 0.05000156909227371, -0.009580492973327637, 0.02807185985147953, -0.009045728482306004, 0.009133345447480679, -0.005226182285696268, 0.01703661121428013, -0.022015279158949852, -0.00795978307723999, 0.033901527523994446, -0.0669899731874466, 0.029679475352168083, 0.04942607879638672, -0.00726829469203949, -0.08165650069713593, 0.01037179958075285, -0.011292658746242523, -0.001186440116725862, 0.023702552542090416, -0.015646353363990784, -0.009631493128836155, 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539 | 539 | ['Antonio Valerio Miceli Barone'] | 1608.02996v1 | Current approaches to learning vector representations of text that are
compatible between different languages usually require some amount of parallel
text, aligned at word, sentence or at least document level. We hypothesize
however, that different natural languages share enough semantic structure that
it should be possible, in principle, to learn compatible vector representations
just by analyzing the monolingual distribution of words.
In order to evaluate this hypothesis, we propose a scheme to map word vectors
trained on a source language to vectors semantically compatible with word
vectors trained on a target language using an adversarial autoencoder.
We present preliminary qualitative results and discuss possible future
developments of this technique, such as applications to cross-lingual sentence
representations. | Towards cross-lingual distributed representations without parallel text
trained with adversarial autoencoders | 2,016 | http://arxiv.org/pdf/1608.02996v1 | Title Towards crosslingual distributed representation without parallel text trained adversarial autoencoders Summary Current approach learning vector representation text compatible different language usually require amount parallel text aligned word sentence least document level hypothesize however different natural language share enough semantic structure possible principle learn compatible vector representation analyzing monolingual distribution word order evaluate hypothesis propose scheme map word vector trained source language vector semantically compatible word vector trained target language using adversarial autoencoder present preliminary qualitative result discus possible future development technique application crosslingual sentence representation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.002654540818184614, 0.029592353850603104, -0.020501552149653435, 0.08953430503606796, -0.05177861452102661, 0.015171200037002563, 0.030116500332951546, -0.03203490003943443, 0.01959400065243244, -0.04761359840631485, -0.051239270716905594, -0.00202724477276206, 0.045413024723529816, -0.007184742484241724, 0.014574458822607994, -0.06497945636510849, 0.034139376133680344, 0.017648521810770035, -0.011516815982758999, -0.04378749430179596, 0.026132695376873016, 0.024162383750081062, 0.009717624634504318, 0.020099468529224396, 0.01093369722366333, -0.033155087381601334, 0.0007729554199613631, -0.045768268406391144, 0.036471325904130936, 0.014158970676362514, 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540 | 540 | ['Zhiyuan Tang', 'Ying Shi', 'Dong Wang', 'Yang Feng', 'Shiyue Zhang'] | 1609.08789v3 | Recurrent neural networks (RNNs) have shown clear superiority in sequence
modeling, particularly the ones with gated units, such as long short-term
memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties
behind the remarkable performance remain unclear in many applications, e.g.,
automatic speech recognition (ASR). This paper employs visualization techniques
to study the behavior of LSTM and GRU when performing speech recognition tasks.
Our experiments show some interesting patterns in the gated memory, and some of
them have inspired simple yet effective modifications on the network structure.
We report two of such modifications: (1) lazy cell update in LSTM, and (2)
shortcut connections for residual learning. Both modifications lead to more
comprehensible and powerful networks. | Memory Visualization for Gated Recurrent Neural Networks in Speech
Recognition | 2,016 | http://arxiv.org/pdf/1609.08789v3 | Title Memory Visualization Gated Recurrent Neural Networks Speech Recognition Summary Recurrent neural network RNNs shown clear superiority sequence modeling particularly one gated unit long shortterm memory LSTM gated recurrent unit GRU However dynamic property behind remarkable performance remain unclear many application eg automatic speech recognition ASR paper employ visualization technique study behavior LSTM GRU performing speech recognition task experiment show interesting pattern gated memory inspired simple yet effective modification network structure report two modification 1 lazy cell update LSTM 2 shortcut connection residual learning modification lead comprehensible powerful network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006197511218488216, 0.0016232029302045703, -0.010341375134885311, 0.04599903151392937, -0.024055778980255127, -0.005096917040646076, 0.060437288135290146, -0.02678726799786091, -0.015349045395851135, 0.0017848003190010786, -0.032852910459041595, -0.04186113923788071, 0.05244435369968414, 0.05691493675112724, 0.035810019820928574, -0.07614907622337341, 0.04726356640458107, -0.029603779315948486, -0.011413116939365864, -0.012069455347955227, 0.021374545991420746, -0.024389106780290604, -0.04370071738958359, 0.04465514421463013, -0.02519134432077408, -0.0017502332339063287, -0.019349966198205948, -0.023544877767562866, 0.008904442191123962, -0.06868797540664673, -0.03759397938847542, 0.009633136913180351, 0.025302216410636902, 0.0363750196993351, 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541 | 541 | ['Hagen Soltau', 'Hank Liao', 'Hasim Sak'] | 1610.09975v1 | We present results that show it is possible to build a competitive, greatly
simplified, large vocabulary continuous speech recognition system with whole
words as acoustic units. We model the output vocabulary of about 100,000 words
directly using deep bi-directional LSTM RNNs with CTC loss. The model is
trained on 125,000 hours of semi-supervised acoustic training data, which
enables us to alleviate the data sparsity problem for word models. We show that
the CTC word models work very well as an end-to-end all-neural speech
recognition model without the use of traditional context-dependent sub-word
phone units that require a pronunciation lexicon, and without any language
model removing the need to decode. We demonstrate that the CTC word models
perform better than a strong, more complex, state-of-the-art baseline with
sub-word units. | Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large
Vocabulary Speech Recognition | 2,016 | http://arxiv.org/pdf/1610.09975v1 | Title Neural Speech Recognizer AcoustictoWord LSTM Model Large Vocabulary Speech Recognition Summary present result show possible build competitive greatly simplified large vocabulary continuous speech recognition system whole word acoustic unit model output vocabulary 100000 word directly using deep bidirectional LSTM RNNs CTC loss model trained 125000 hour semisupervised acoustic training data enables u alleviate data sparsity problem word model show CTC word model work well endtoend allneural speech recognition model without use traditional contextdependent subword phone unit require pronunciation lexicon without language model removing need decode demonstrate CTC word model perform better strong complex stateoftheart baseline subword unit Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.004185554105788469, 0.043656352907419205, 0.04244375228881836, 0.09234080463647842, -0.003420830937102437, 0.00681381905451417, 0.03762492164969444, 0.013959381729364395, -0.03840254992246628, -0.021074889227747917, -0.0640619620680809, -0.03905227780342102, 0.03335243836045265, 0.04141192510724068, 0.010639674961566925, -0.029731636866927147, 0.00884169340133667, -0.03682668134570122, 0.018027471378445625, -0.023662224411964417, 0.014224696904420853, 0.006339931860566139, -0.043488264083862305, 0.02370227687060833, -0.013264423236250877, 0.01014526654034853, -0.004385205917060375, -0.003694952465593815, -0.02159620262682438, -0.05815283954143524, 0.021591652184724808, 0.022364890202879906, 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542 | 542 | ['Prajit Ramachandran', 'Peter J. Liu', 'Quoc V. Le'] | 1611.02683v2 | This work presents a general unsupervised learning method to improve the
accuracy of sequence to sequence (seq2seq) models. In our method, the weights
of the encoder and decoder of a seq2seq model are initialized with the
pretrained weights of two language models and then fine-tuned with labeled
data. We apply this method to challenging benchmarks in machine translation and
abstractive summarization and find that it significantly improves the
subsequent supervised models. Our main result is that pretraining improves the
generalization of seq2seq models. We achieve state-of-the art results on the
WMT English$\rightarrow$German task, surpassing a range of methods using both
phrase-based machine translation and neural machine translation. Our method
achieves a significant improvement of 1.3 BLEU from the previous best models on
both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human
evaluations on abstractive summarization and find that our method outperforms a
purely supervised learning baseline in a statistically significant manner. | Unsupervised Pretraining for Sequence to Sequence Learning | 2,016 | http://arxiv.org/pdf/1611.02683v2 | Title Unsupervised Pretraining Sequence Sequence Learning Summary work present general unsupervised learning method improve accuracy sequence sequence seq2seq model method weight encoder decoder seq2seq model initialized pretrained weight two language model finetuned labeled data apply method challenging benchmark machine translation abstractive summarization find significantly improves subsequent supervised model main result pretraining improves generalization seq2seq model achieve stateofthe art result WMT EnglishrightarrowGerman task surpassing range method using phrasebased machine translation neural machine translation method achieves significant improvement 13 BLEU previous best model WMT14 WMT15 EnglishrightarrowGerman also conduct human evaluation abstractive summarization find method outperforms purely supervised learning baseline statistically significant manner Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.002003306522965431, 0.06992283463478088, -0.011045689694583416, 0.01799277774989605, -0.0049300226382911205, 0.02622196078300476, 0.021231332793831825, -0.005262626335024834, -0.029905002564191818, -0.045856647193431854, -0.029438117519021034, -0.013366500847041607, 0.030526725575327873, 0.03710099309682846, 0.019275348633527756, -0.0738009586930275, 0.011646273545920849, 0.01669241674244404, -0.003259263699874282, 0.018370414152741432, 0.015542177483439445, 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543 | 543 | ['Yoon Kim', 'Carl Denton', 'Luong Hoang', 'Alexander M. Rush'] | 1702.00887v3 | Attention networks have proven to be an effective approach for embedding
categorical inference within a deep neural network. However, for many tasks we
may want to model richer structural dependencies without abandoning end-to-end
training. In this work, we experiment with incorporating richer structural
distributions, encoded using graphical models, within deep networks. We show
that these structured attention networks are simple extensions of the basic
attention procedure, and that they allow for extending attention beyond the
standard soft-selection approach, such as attending to partial segmentations or
to subtrees. We experiment with two different classes of structured attention
networks: a linear-chain conditional random field and a graph-based parsing
model, and describe how these models can be practically implemented as neural
network layers. Experiments show that this approach is effective for
incorporating structural biases, and structured attention networks outperform
baseline attention models on a variety of synthetic and real tasks: tree
transduction, neural machine translation, question answering, and natural
language inference. We further find that models trained in this way learn
interesting unsupervised hidden representations that generalize simple
attention. | Structured Attention Networks | 2,017 | http://arxiv.org/pdf/1702.00887v3 | Title Structured Attention Networks Summary Attention network proven effective approach embedding categorical inference within deep neural network However many task may want model richer structural dependency without abandoning endtoend training work experiment incorporating richer structural distribution encoded using graphical model within deep network show structured attention network simple extension basic attention procedure allow extending attention beyond standard softselection approach attending partial segmentation subtrees experiment two different class structured attention network linearchain conditional random field graphbased parsing model describe model practically implemented neural network layer Experiments show approach effective incorporating structural bias structured attention network outperform baseline attention model variety synthetic real task tree transduction neural machine translation question answering natural language inference find model trained way learn interesting unsupervised hidden representation generalize simple attention Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.026088450103998184, 0.0376410074532032, -0.01531126070767641, 0.04309573397040367, -0.001533530536107719, -0.003978844732046127, -0.0019318856066092849, -0.026550261303782463, -0.006426883861422539, -0.05096250772476196, -0.009327799081802368, 0.032912496477365494, -0.0024358744267374277, 0.05665376037359238, 0.02705538645386696, -0.10513221472501755, 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544 | 544 | ['Hongyu Guo', 'Colin Cherry', 'Jiang Su'] | 1704.05907v1 | We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks. | End-to-End Multi-View Networks for Text Classification | 2,017 | http://arxiv.org/pdf/1704.05907v1 | Title EndtoEnd MultiView Networks Text Classification Summary propose multiview network text classification method automatically creates various view input text taking form soft attention weight distribute classifier focus among set base feature bagofwords representation view focus different subset text word Aggregating many view result discriminative robust representation novel architecture stack concatenates view produce network emphasizes depth width allowing training converge quickly Using multiview architecture establish new stateoftheart accuracy two benchmark task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao 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545 | 545 | ['Kartik Goyal', 'Chris Dyer', 'Taylor Berg-Kirkpatrick'] | 1704.06970v1 | We demonstrate that a continuous relaxation of the argmax operation can be
used to create a differentiable approximation to greedy decoding for
sequence-to-sequence (seq2seq) models. By incorporating this approximation into
the scheduled sampling training procedure (Bengio et al., 2015)--a well-known
technique for correcting exposure bias--we introduce a new training objective
that is continuous and differentiable everywhere and that can provide
informative gradients near points where previous decoding decisions change
their value. In addition, by using a related approximation, we demonstrate a
similar approach to sampled-based training. Finally, we show that our approach
outperforms cross-entropy training and scheduled sampling procedures in two
sequence prediction tasks: named entity recognition and machine translation. | Differentiable Scheduled Sampling for Credit Assignment | 2,017 | http://arxiv.org/pdf/1704.06970v1 | Title Differentiable Scheduled Sampling Credit Assignment Summary demonstrate continuous relaxation argmax operation used create differentiable approximation greedy decoding sequencetosequence seq2seq model incorporating approximation scheduled sampling training procedure Bengio et al 2015a wellknown technique correcting exposure biaswe introduce new training objective continuous differentiable everywhere provide informative gradient near point previous decoding decision change value addition using related approximation demonstrate similar approach sampledbased training Finally show approach outperforms crossentropy training scheduled sampling procedure two sequence prediction task named entity recognition machine translation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04359620064496994, 0.042325232177972794, 0.006403833162039518, 0.015020525082945824, 0.009850161150097847, 0.05608697980642319, -0.016278481110930443, 0.005217379424721003, -0.014566252939403057, -0.017947543412446976, -0.022926131263375282, -0.0151705052703619, 0.018968705087900162, -0.028447363525629044, 0.0015770156169310212, -0.049606189131736755, 0.021832425147294998, 0.015778806060552597, -0.026257440447807312, 0.01504603587090969, -0.004881075583398342, 0.009638192132115364, -0.010514524765312672, 0.011675076559185982, 0.04967228323221207, 0.005397742614150047, -0.01159322913736105, 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546 | 546 | ['Zhiyuan Tang', 'Dong Wang', 'Yixiang Chen', 'Ying Shi', 'Lantian Li'] | 1705.03152v2 | Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective. | Phone-aware Neural Language Identification | 2,017 | http://arxiv.org/pdf/1705.03152v2 | Title Phoneaware Neural Language Identification Summary Pure acoustic neural model particularly LSTMRNN model shown great potential language identification LID However phonetic information largely overlooked existing neural LID model although information used conventional phonetic LID system great success present phoneaware neural LID architecture deep LSTMRNN LID system accepts output RNNbased ASR system utilizing phonetic knowledge LID performance significantly improved Interestingly even test language involved ASR training phonetic knowledge still present large contribution experiment conducted four language within Babel corpus demonstrated phoneaware approach highly effective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01298956386744976, 0.08186398446559906, 0.015031697228550911, 0.04583071172237396, -0.013382386416196823, 0.01289944164454937, 0.055056098848581314, -0.01047565694898367, -0.016969528049230576, -0.0007712991209700704, -0.06293878704309464, -0.04870281741023064, 0.05759785696864128, 0.0004911783616989851, 0.04379619285464287, 0.012853295542299747, -0.007544956635683775, -0.04623204469680786, 0.033887267112731934, -0.02763558179140091, 0.020136451348662376, -0.0032870969735085964, -0.04453730955719948, 0.04388202354311943, -0.014970695599913597, 0.01690002903342247, -0.017691807821393013, 0.018701551482081413, -0.035239264369010925, -0.03309350833296776, 0.024562202394008636, 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547 | 547 | ['Marek Rei'] | 1707.05233v1 | Automated methods for essay scoring have made great progress in recent years,
achieving accuracies very close to human annotators. However, a known weakness
of such automated scorers is not taking into account the semantic relevance of
the submitted text. While there is existing work on detecting answer relevance
given a textual prompt, very little previous research has been done to
incorporate visual writing prompts. We propose a neural architecture and
several extensions for detecting off-topic responses to visual prompts and
evaluate it on a dataset of texts written by language learners. | Detecting Off-topic Responses to Visual Prompts | 2,017 | http://arxiv.org/pdf/1707.05233v1 | Title Detecting Offtopic Responses Visual Prompts Summary Automated method essay scoring made great progress recent year achieving accuracy close human annotator However known weakness automated scorer taking account semantic relevance submitted text existing work detecting answer relevance given textual prompt little previous research done incorporate visual writing prompt propose neural architecture several extension detecting offtopic response visual prompt evaluate dataset text written language learner Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.10225941240787506, -0.0015375548973679543, -0.01723756641149521, 0.03070058673620224, -0.0012213430600240827, 0.011814624071121216, 0.002692685928195715, 0.06981052458286285, 0.017407312989234924, -0.04312202334403992, 0.012537005357444286, 0.007268738467246294, -0.010276996530592442, 0.06191529333591461, -0.03037123754620552, -0.04784541204571724, 0.015811476856470108, -0.0027892938815057278, 0.051254209131002426, 0.022359225898981094, 0.0369633287191391, 0.0234584528952837, -0.019932305440306664, 0.04621809348464012, 0.0016196412034332752, -0.04123590886592865, 0.005213411990553141, -0.0038773019332438707, 0.015069388784468174, -0.01753491535782814, 0.041166260838508606, 0.08502021431922913, -0.0537739172577858, 0.025423312559723854, 1.942734115800704e-06, -0.0431973822414875, 0.00443617906421423, -0.06493992358446121, 0.002272185869514942, 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548 | 548 | ['Fréderic Godin', 'Jonas Degrave', 'Joni Dambre', 'Wesley De Neve'] | 1707.08214v2 | In this paper, we introduce a novel type of Rectified Linear Unit (ReLU),
called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an
unbounded positive and negative image, can be used as a drop-in replacement for
a tanh activation function in the recurrent step of Quasi-Recurrent Neural
Networks (QRNNs) (Bradbury et al. (2017)). Similar to ReLUs, DReLUs are less
prone to the vanishing gradient problem, they are noise robust, and they induce
sparse activations.
We independently reproduce the QRNN experiments of Bradbury et al. (2017) and
compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long
Short-Term Memory networks (LSTMs) on sentiment classification and word-level
language modeling. Additionally, we evaluate on character-level language
modeling, showing that we are able to stack up to eight QRNN layers with
DReLUs, thus making it possible to improve the current state-of-the-art in
character-level language modeling over shallow architectures based on LSTMs. | Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation
Functions in Quasi-Recurrent Neural Networks | 2,017 | http://arxiv.org/pdf/1707.08214v2 | Title Dual Rectified Linear Units DReLUs Replacement Tanh Activation Functions QuasiRecurrent Neural Networks Summary paper introduce novel type Rectified Linear Unit ReLU called Dual Rectified Linear Unit DReLU DReLU come unbounded positive negative image used dropin replacement tanh activation function recurrent step QuasiRecurrent Neural Networks QRNNs Bradbury et al 2017 Similar ReLUs DReLUs le prone vanishing gradient problem noise robust induce sparse activation independently reproduce QRNN experiment Bradbury et al 2017 compare DReLUbased QRNNs original tanhbased QRNNs Long ShortTerm Memory network LSTMs sentiment classification wordlevel language modeling Additionally evaluate characterlevel language modeling showing able stack eight QRNN layer DReLUs thus making possible improve current stateoftheart characterlevel language modeling shallow architecture based LSTMs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0021918551065027714, 0.008251101709902287, 0.01522910874336958, 0.04784897714853287, -0.017525488510727882, 0.005926362704485655, 0.02409275807440281, -0.007333395536988974, -0.02322854846715927, -0.036688949912786484, -0.053567834198474884, -0.06477624177932739, 0.007544497959315777, 0.07392292469739914, -0.010645050555467606, -0.02223210223019123, 0.03723742440342903, 0.02068973332643509, 0.028928017243742943, -0.025223081931471825, 0.019118938595056534, -0.013045801781117916, 0.00594067107886076, 0.0034662496764212847, -0.00021584640489891171, 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549 | 549 | ['Mostafa Dehghani', 'Arash Mehrjou', 'Stephan Gouws', 'Jaap Kamps', 'Bernhard Schölkopf'] | 1711.02799v1 | Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations. | Fidelity-Weighted Learning | 2,017 | http://arxiv.org/pdf/1711.02799v1 | Title FidelityWeighted Learning Summary Training deep neural network requires many training sample practice training label expensive obtain may varying quality may trusted expert labelers others might heuristic source weak supervision crowdsourcing creates fundamental quality versusquantity tradeoff learning process learn small amount highquality data potentially large amount weaklylabeled data argue learner could somehow know take labelquality account learning data representation could get best world end propose fidelityweighted learning FWL semisupervised studentteacher approach training deep neural network using weaklylabeled data FWL modulates parameter update student network trained task care persample basis according posterior confidence labelquality estimated teacher access highquality label student teacher learned data evaluate FWL two task information retrieval natural language processing outperform stateoftheart alternative semisupervised method indicating approach make better use strong weak label lead better taskdependent data representation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05056807026267052, 0.05870668217539787, -0.02262929268181324, 0.04009771719574928, 0.021441899240016937, -0.03986431658267975, 0.03014550544321537, -0.03484506532549858, -0.014632077887654305, -0.01139342412352562, -0.04413081333041191, -0.0247972309589386, 0.007972529157996178, 0.057504575699567795, 0.009582389146089554, -0.06808078289031982, 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550 | 550 | ['Dong Yu', 'Michael L. Seltzer', 'Jinyu Li', 'Jui-Ting Huang', 'Frank Seide'] | 1301.3605v3 | Recent studies have shown that deep neural networks (DNNs) perform
significantly better than shallow networks and Gaussian mixture models (GMMs)
on large vocabulary speech recognition tasks. In this paper, we argue that the
improved accuracy achieved by the DNNs is the result of their ability to
extract discriminative internal representations that are robust to the many
sources of variability in speech signals. We show that these representations
become increasingly insensitive to small perturbations in the input with
increasing network depth, which leads to better speech recognition performance
with deeper networks. We also show that DNNs cannot extrapolate to test samples
that are substantially different from the training examples. If the training
data are sufficiently representative, however, internal features learned by the
DNN are relatively stable with respect to speaker differences, bandwidth
differences, and environment distortion. This enables DNN-based recognizers to
perform as well or better than state-of-the-art systems based on GMMs or
shallow networks without the need for explicit model adaptation or feature
normalization. | Feature Learning in Deep Neural Networks - Studies on Speech Recognition
Tasks | 2,013 | http://arxiv.org/pdf/1301.3605v3 | Title Feature Learning Deep Neural Networks Studies Speech Recognition Tasks Summary Recent study shown deep neural network DNNs perform significantly better shallow network Gaussian mixture model GMMs large vocabulary speech recognition task paper argue improved accuracy achieved DNNs result ability extract discriminative internal representation robust many source variability speech signal show representation become increasingly insensitive small perturbation input increasing network depth lead better speech recognition performance deeper network also show DNNs cannot extrapolate test sample substantially different training example training data sufficiently representative however internal feature learned DNN relatively stable respect speaker difference bandwidth difference environment distortion enables DNNbased recognizers perform well better stateoftheart system based GMMs shallow network without need explicit model adaptation feature normalization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017452381551265717, -0.002058636164292693, 0.0036066260654479265, 0.03968363255262375, -0.007213184610009193, -0.017433112487196922, 0.05981472134590149, -0.017009399831295013, -0.04588581249117851, 0.01918136700987816, -0.041604217141866684, 0.01103188656270504, 0.04672212898731232, 0.03411168232560158, 0.011865508742630482, -0.07304758578538895, 0.00027025179588235915, -0.014732200652360916, -0.012955323792994022, -0.03591141477227211, 0.019875332713127136, -0.009360787458717823, -0.04264940693974495, 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551 | 551 | ['Dimitri Palaz', 'Ronan Collobert', 'Mathew Magimai. -Doss'] | 1304.1018v2 | In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic
speech recognition (ASR) system, the phoneme class conditional probabilities
are estimated by first extracting acoustic features from the speech signal
based on prior knowledge such as, speech perception or/and speech production
knowledge, and, then modeling the acoustic features with an ANN. Recent
advances in machine learning techniques, more specifically in the field of
image processing and text processing, have shown that such divide and conquer
strategy (i.e., separating feature extraction and modeling steps) may not be
necessary. Motivated from these studies, in the framework of convolutional
neural networks (CNNs), this paper investigates a novel approach, where the
input to the ANN is raw speech signal and the output is phoneme class
conditional probability estimates. On TIMIT phoneme recognition task, we study
different ANN architectures to show the benefit of CNNs and compare the
proposed approach against conventional approach where, spectral-based feature
MFCC is extracted and modeled by a multilayer perceptron. Our studies show that
the proposed approach can yield comparable or better phoneme recognition
performance when compared to the conventional approach. It indicates that CNNs
can learn features relevant for phoneme classification automatically from the
raw speech signal. | Estimating Phoneme Class Conditional Probabilities from Raw Speech
Signal using Convolutional Neural Networks | 2,013 | http://arxiv.org/pdf/1304.1018v2 | Title Estimating Phoneme Class Conditional Probabilities Raw Speech Signal using Convolutional Neural Networks Summary hybrid hidden Markov modelartificial neural network HMMANN automatic speech recognition ASR system phoneme class conditional probability estimated first extracting acoustic feature speech signal based prior knowledge speech perception orand speech production knowledge modeling acoustic feature ANN Recent advance machine learning technique specifically field image processing text processing shown divide conquer strategy ie separating feature extraction modeling step may necessary Motivated study framework convolutional neural network CNNs paper investigates novel approach input ANN raw speech signal output phoneme class conditional probability estimate TIMIT phoneme recognition task study different ANN architecture show benefit CNNs compare proposed approach conventional approach spectralbased feature MFCC extracted modeled multilayer perceptron study show proposed approach yield comparable better phoneme recognition performance compared conventional approach indicates CNNs learn feature relevant phoneme classification automatically raw speech signal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.013593069277703762, 0.04590294510126114, 0.017292046919465065, 0.0597132183611393, 0.004294393118470907, -0.019776178523898125, 0.04187477007508278, 0.006013117264956236, -0.034289129078388214, -0.03814668580889702, -0.0364546962082386, -0.013362758792936802, 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552 | 552 | ['Samuel R. Bowman', 'Christopher Potts', 'Christopher D. Manning'] | 1406.1827v4 | Tree-structured recursive neural networks (TreeRNNs) for sentence meaning
have been successful for many applications, but it remains an open question
whether the fixed-length representations that they learn can support tasks as
demanding as logical deduction. We pursue this question by evaluating whether
two such models---plain TreeRNNs and tree-structured neural tensor networks
(TreeRNTNs)---can correctly learn to identify logical relationships such as
entailment and contradiction using these representations. In our first set of
experiments, we generate artificial data from a logical grammar and use it to
evaluate the models' ability to learn to handle basic relational reasoning,
recursive structures, and quantification. We then evaluate the models on the
more natural SICK challenge data. Both models perform competitively on the SICK
data and generalize well in all three experiments on simulated data, suggesting
that they can learn suitable representations for logical inference in natural
language. | Recursive Neural Networks Can Learn Logical Semantics | 2,014 | http://arxiv.org/pdf/1406.1827v4 | Title Recursive Neural Networks Learn Logical Semantics Summary Treestructured recursive neural network TreeRNNs sentence meaning successful many application remains open question whether fixedlength representation learn support task demanding logical deduction pursue question evaluating whether two modelsplain TreeRNNs treestructured neural tensor network TreeRNTNscan correctly learn identify logical relationship entailment contradiction using representation first set experiment generate artificial data logical grammar use evaluate model ability learn handle basic relational reasoning recursive structure quantification evaluate model natural SICK challenge data model perform competitively SICK data generalize well three experiment simulated data suggesting learn suitable representation logical inference natural language Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04599861800670624, 0.04760322719812393, -0.021887384355068207, 0.05416524410247803, -0.05406181514263153, 0.007405961398035288, -0.010208075866103172, -0.01070459932088852, 0.0029791484121233225, -0.04488600790500641, 0.039833322167396545, 0.03173527494072914, 0.006863091140985489, 0.051395632326602936, 0.0032243358436971903, -0.025278303772211075, 0.03980003669857979, 0.018396740779280663, 0.019822372123599052, 0.006317878141999245, -0.006075665354728699, 0.03436284884810448, -0.003954057116061449, 0.02591763064265251, -0.010520695708692074, -0.035730332136154175, 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553 | 553 | ['Chenxi Zhu', 'Xipeng Qiu', 'Xinchi Chen', 'Xuanjing Huang'] | 1505.05667v1 | In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets. | A Re-ranking Model for Dependency Parser with Recursive Convolutional
Neural Network | 2,015 | http://arxiv.org/pdf/1505.05667v1 | Title Reranking Model Dependency Parser Recursive Convolutional Neural Network Summary work address problem model node word phrase dependency tree dense representation propose recursive convolutional neural network RCNN architecture capture syntactic compositionalsemantic representation phrase word dependency tree Different original recursive neural network introduce convolution pooling layer model variety composition feature map choose informative composition pooling layer Based RCNN use discriminative model rerank kbest list candidate dependency parsing tree experiment show RCNN effective improve stateoftheart dependency parsing English Chinese datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.058216795325279236, 0.036891523748636246, 0.007242720574140549, 0.09531322866678238, -0.04551072418689728, -2.6305309802410193e-05, -0.04743984714150429, -0.008480262942612171, -0.007531933486461639, -0.03914311155676842, 0.0289884265512228, 0.016147201880812645, 0.030944496393203735, 0.022281188517808914, -0.046962521970272064, -0.06445243954658508, 0.014898279681801796, 0.061139706522226334, -0.013975940644741058, 0.02628675475716591, 0.040750738233327866, 0.04359287768602371, 0.022342832759022713, 0.026446104049682617, -0.04429510608315468, -0.05500749871134758, 0.02537626028060913, 0.005224676337093115, -0.03482000529766083, -0.05483456701040268, 0.05070527270436287, 0.03619440644979477, 0.015609447844326496, 0.031908851116895676, 2.3401330508932006e-06, -0.031242918223142624, 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554 | 554 | ['Lantian Li', 'Dong Wang', 'Zhiyong Zhang', 'Thomas Fang Zheng'] | 1505.06427v1 | Recent research shows that deep neural networks (DNNs) can be used to extract
deep speaker vectors (d-vectors) that preserve speaker characteristics and can
be used in speaker verification. This new method has been tested on
text-dependent speaker verification tasks, and improvement was reported when
combined with the conventional i-vector method.
This paper extends the d-vector approach to semi text-independent speaker
verification tasks, i.e., the text of the speech is in a limited set of short
phrases. We explore various settings of the DNN structure used for d-vector
extraction, and present a phone-dependent training which employs the posterior
features obtained from an ASR system. The experimental results show that it is
possible to apply d-vectors on semi text-independent speaker recognition, and
the phone-dependent training improves system performance. | Deep Speaker Vectors for Semi Text-independent Speaker Verification | 2,015 | http://arxiv.org/pdf/1505.06427v1 | Title Deep Speaker Vectors Semi Textindependent Speaker Verification Summary Recent research show deep neural network DNNs used extract deep speaker vector dvectors preserve speaker characteristic used speaker verification new method tested textdependent speaker verification task improvement reported combined conventional ivector method paper extends dvector approach semi textindependent speaker verification task ie text speech limited set short phrase explore various setting DNN structure used dvector extraction present phonedependent training employ posterior feature obtained ASR system experimental result show possible apply dvectors semi textindependent speaker recognition phonedependent training improves system performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.004094146192073822, 0.025912322103977203, -0.00012574568972922862, 0.06336522102355957, -0.013480176217854023, 0.013753141276538372, 0.05886764079332352, -0.011309025809168816, -0.015574604272842407, -0.005354780238121748, -0.0016111833974719048, -0.03165379539132118, 0.02726726606488228, -0.008409736678004265, 0.0038546451833099127, -0.05365271493792534, -0.028427284210920334, 0.01971697434782982, 0.03773965686559677, -0.029848255217075348, 0.04130707308650017, -0.015582097694277763, -0.0542699359357357, -0.0017285828944295645, -0.04834470525383949, 0.010565456002950668, 0.00013589851732831448, 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555 | 555 | ['Tom Sercu', 'Vaibhava Goel'] | 1604.01792v2 | Very deep CNNs with small 3x3 kernels have recently been shown to achieve
very strong performance as acoustic models in hybrid NN-HMM speech recognition
systems. In this paper we investigate how to efficiently scale these models to
larger datasets. Specifically, we address the design choice of pooling and
padding along the time dimension which renders convolutional evaluation of
sequences highly inefficient. We propose a new CNN design without timepadding
and without timepooling, which is slightly suboptimal for accuracy, but has two
significant advantages: it enables sequence training and deployment by allowing
efficient convolutional evaluation of full utterances, and, it allows for batch
normalization to be straightforwardly adopted to CNNs on sequence data. Through
batch normalization, we recover the lost peformance from removing the
time-pooling, while keeping the benefit of efficient convolutional evaluation.
We demonstrate the performance of our models both on larger scale data than
before, and after sequence training. Our very deep CNN model sequence trained
on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5
test-set, matching with a single model the performance of the 2015 IBM system
combination, which was the previous best published result. | Advances in Very Deep Convolutional Neural Networks for LVCSR | 2,016 | http://arxiv.org/pdf/1604.01792v2 | Title Advances Deep Convolutional Neural Networks LVCSR Summary deep CNNs small 3x3 kernel recently shown achieve strong performance acoustic model hybrid NNHMM speech recognition system paper investigate efficiently scale model larger datasets Specifically address design choice pooling padding along time dimension render convolutional evaluation sequence highly inefficient propose new CNN design without timepadding without timepooling slightly suboptimal accuracy two significant advantage enables sequence training deployment allowing efficient convolutional evaluation full utterance allows batch normalization straightforwardly adopted CNNs sequence data batch normalization recover lost peformance removing timepooling keeping benefit efficient convolutional evaluation demonstrate performance model larger scale data sequence training deep CNN model sequence trained 2000h switchboard dataset obtains 94 word error rate Hub5 testset matching single model performance 2015 IBM system combination previous best published result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0027890349738299847, 0.029791463166475296, 0.027223581448197365, 0.0857696682214737, 0.010207554325461388, -0.03195658326148987, 0.05236981064081192, -0.02051599696278572, -0.027545113116502762, 0.01005551777780056, -0.06834647059440613, -0.022901779040694237, 0.012852547690272331, 0.035293564200401306, 0.02351105585694313, 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556 | 556 | ['Zhiyun Lu', 'Vikas Sindhwani', 'Tara N. Sainath'] | 1604.02594v1 | Recurrent neural networks (RNNs), including long short-term memory (LSTM)
RNNs, have produced state-of-the-art results on a variety of speech recognition
tasks. However, these models are often too large in size for deployment on
mobile devices with memory and latency constraints. In this work, we study
mechanisms for learning compact RNNs and LSTMs via low-rank factorizations and
parameter sharing schemes. Our goal is to investigate redundancies in recurrent
architectures where compression can be admitted without losing performance. A
hybrid strategy of using structured matrices in the bottom layers and shared
low-rank factors on the top layers is found to be particularly effective,
reducing the parameters of a standard LSTM by 75%, at a small cost of 0.3%
increase in WER, on a 2,000-hr English Voice Search task. | Learning Compact Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1604.02594v1 | Title Learning Compact Recurrent Neural Networks Summary Recurrent neural network RNNs including long shortterm memory LSTM RNNs produced stateoftheart result variety speech recognition task However model often large size deployment mobile device memory latency constraint work study mechanism learning compact RNNs LSTMs via lowrank factorization parameter sharing scheme goal investigate redundancy recurrent architecture compression admitted without losing performance hybrid strategy using structured matrix bottom layer shared lowrank factor top layer found particularly effective reducing parameter standard LSTM 75 small cost 03 increase WER 2000hr English Voice Search task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0004871900600846857, -0.004935301840305328, 0.0123648876324296, 0.05660497397184372, 0.00821257196366787, -0.031614307314157486, 0.04056230187416077, -0.0007642898708581924, -0.02976626344025135, -0.010106392204761505, -0.04787039756774902, -0.03563445433974266, 0.03354519233107567, -0.0073138996958732605, -0.01384778507053852, -0.01619875803589821, 0.014862331561744213, 0.0028848478104919195, -0.020567433908581734, -0.0065873670391738415, 0.003429671749472618, -0.0011282333871349692, 0.004578140564262867, 0.047108687460422516, 0.02356795035302639, 0.02338140271604061, -0.01965482346713543, -0.006328277289867401, -0.008486073464155197, -0.050640031695365906, -0.008799524046480656, 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557 | 557 | ['Adhiguna Kuncoro', 'Yuichiro Sawai', 'Kevin Duh', 'Yuji Matsumoto'] | 1604.06529v2 | We propose a transition-based dependency parser using Recurrent Neural
Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward
neural network parser of Chen and Manning (2014) and enables modelling of
entire sequences of shift/reduce transition decisions. On the Google Web
Treebank, our LSTM parser is competitive with the best feedforward parser on
overall accuracy and notably achieves more than 3% improvement for long-range
dependencies, which has proved difficult for previous transition-based parsers
due to error propagation and limited context information. Our findings
additionally suggest that dropout regularisation on the embedding layer is
crucial to improve the LSTM's generalisation. | Dependency Parsing with LSTMs: An Empirical Evaluation | 2,016 | http://arxiv.org/pdf/1604.06529v2 | Title Dependency Parsing LSTMs Empirical Evaluation Summary propose transitionbased dependency parser using Recurrent Neural Networks Long ShortTerm Memory LSTM unit extends feedforward neural network parser Chen Manning 2014 enables modelling entire sequence shiftreduce transition decision Google Web Treebank LSTM parser competitive best feedforward parser overall accuracy notably achieves 3 improvement longrange dependency proved difficult previous transitionbased parser due error propagation limited context information finding additionally suggest dropout regularisation embedding layer crucial improve LSTMs generalisation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.029366113245487213, 0.05464070290327072, -0.003604850498959422, 0.047632377594709396, -0.03991984203457832, -0.0028011994436383247, -0.025996096432209015, -0.013133931905031204, -0.025421075522899628, -0.042501360177993774, 0.03368372842669487, -0.010501038283109665, 0.012196731753647327, 0.03819626569747925, -0.04327261820435524, -0.08870454132556915, 0.022979727014899254, 0.01921197399497032, -0.004901285283267498, 0.06115353852510452, 0.012805238366127014, 0.010281271301209927, 0.005892943125218153, 0.030362116172909737, -0.029457155615091324, -0.04579217731952667, -0.003942452371120453, -0.018264828249812126, -0.008555086329579353, -0.09741108864545822, 0.028322430327534676, 0.03640725836157799, 0.03409162163734436, 0.02023167349398136, 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558 | 558 | ['Hamid Palangi', 'Li Deng', 'Yelong Shen', 'Jianfeng Gao', 'Xiaodong He', 'Jianshu Chen', 'Xinying Song', 'Rabab Ward'] | 1502.06922v3 | This paper develops a model that addresses sentence embedding, a hot topic in
current natural language processing research, using recurrent neural networks
with Long Short-Term Memory (LSTM) cells. Due to its ability to capture long
term memory, the LSTM-RNN accumulates increasingly richer information as it
goes through the sentence, and when it reaches the last word, the hidden layer
of the network provides a semantic representation of the whole sentence. In
this paper, the LSTM-RNN is trained in a weakly supervised manner on user
click-through data logged by a commercial web search engine. Visualization and
analysis are performed to understand how the embedding process works. The model
is found to automatically attenuate the unimportant words and detects the
salient keywords in the sentence. Furthermore, these detected keywords are
found to automatically activate different cells of the LSTM-RNN, where words
belonging to a similar topic activate the same cell. As a semantic
representation of the sentence, the embedding vector can be used in many
different applications. These automatic keyword detection and topic allocation
abilities enabled by the LSTM-RNN allow the network to perform document
retrieval, a difficult language processing task, where the similarity between
the query and documents can be measured by the distance between their
corresponding sentence embedding vectors computed by the LSTM-RNN. On a web
search task, the LSTM-RNN embedding is shown to significantly outperform
several existing state of the art methods. We emphasize that the proposed model
generates sentence embedding vectors that are specially useful for web document
retrieval tasks. A comparison with a well known general sentence embedding
method, the Paragraph Vector, is performed. The results show that the proposed
method in this paper significantly outperforms it for web document retrieval
task. | Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis
and Application to Information Retrieval | 2,015 | http://arxiv.org/pdf/1502.06922v3 | Title Deep Sentence Embedding Using Long ShortTerm Memory Networks Analysis Application Information Retrieval Summary paper develops model address sentence embedding hot topic current natural language processing research using recurrent neural network Long ShortTerm Memory LSTM cell Due ability capture long term memory LSTMRNN accumulates increasingly richer information go sentence reach last word hidden layer network provides semantic representation whole sentence paper LSTMRNN trained weakly supervised manner user clickthrough data logged commercial web search engine Visualization analysis performed understand embedding process work model found automatically attenuate unimportant word detects salient keywords sentence Furthermore detected keywords found automatically activate different cell LSTMRNN word belonging similar topic activate cell semantic representation sentence embedding vector used many different application automatic keyword detection topic allocation ability enabled LSTMRNN allow network perform document retrieval difficult language processing task similarity query document measured distance corresponding sentence embedding vector computed LSTMRNN web search task LSTMRNN embedding shown significantly outperform several existing state art method emphasize proposed model generates sentence embedding vector specially useful web document retrieval task comparison well known general sentence embedding method Paragraph Vector performed result show proposed method paper significantly outperforms web document retrieval task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03156965970993042, -0.04089318588376045, -0.008375040255486965, 0.05650141090154648, -0.025587432086467743, 0.006287709344178438, 0.002414876129478216, -0.021042458713054657, 0.010764251463115215, -0.06723520904779434, -0.004185568541288376, 0.003493493190035224, -0.010016089305281639, 0.012699143029749393, 0.018240852281451225, -0.009216025471687317, 0.020172759890556335, 0.0314435213804245, 0.018552135676145554, 0.048415202647447586, 0.045910827815532684, 0.008736995048820972, 0.03160793334245682, 0.04126831144094467, 0.015903428196907043, 0.0035375258885324, -0.007645900826901197, -0.02888103388249874, -0.008391764014959335, -0.055223990231752396, 0.0524895042181015, -0.003478875383734703, 0.07039263844490051, -0.004584906622767448, 1.9714259451575344e-06, -0.010946686379611492, -0.025401605293154716, -0.04412001743912697, 0.014067563228309155, -0.0033294185996055603, 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559 | 559 | ['Fandong Meng', 'Zhengdong Lu', 'Mingxuan Wang', 'Hang Li', 'Wenbin Jiang', 'Qun Liu'] | 1503.01838v5 | The recently proposed neural network joint model (NNJM) (Devlin et al., 2014)
augments the n-gram target language model with a heuristically chosen source
context window, achieving state-of-the-art performance in SMT. In this paper,
we give a more systematic treatment by summarizing the relevant source
information through a convolutional architecture guided by the target
information. With different guiding signals during decoding, our specifically
designed convolution+gating architectures can pinpoint the parts of a source
sentence that are relevant to predicting a target word, and fuse them with the
context of entire source sentence to form a unified representation. This
representation, together with target language words, are fed to a deep neural
network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English
translation tasks show that the proposed model can achieve significant
improvements over the previous NNJM by up to +1.08 BLEU points on average | Encoding Source Language with Convolutional Neural Network for Machine
Translation | 2,015 | http://arxiv.org/pdf/1503.01838v5 | Title Encoding Source Language Convolutional Neural Network Machine Translation Summary recently proposed neural network joint model NNJM Devlin et al 2014 augments ngram target language model heuristically chosen source context window achieving stateoftheart performance SMT paper give systematic treatment summarizing relevant source information convolutional architecture guided target information different guiding signal decoding specifically designed convolutiongating architecture pinpoint part source sentence relevant predicting target word fuse context entire source sentence form unified representation representation together target language word fed deep neural network DNN form stronger NNJM Experiments two NIST ChineseEnglish translation task show proposed model achieve significant improvement previous NNJM 108 BLEU point average Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05548024922609329, 0.047425027936697006, 0.014198657125234604, 0.07506497204303741, -0.03541373834013939, 0.012434015981853008, 0.039030659943819046, 0.004686171654611826, -0.07581836730241776, -0.029282188042998314, -0.05318759009242058, -0.02025393396615982, 0.005503562744706869, 0.04283221811056137, 0.023977186530828476, -0.05204669386148453, 0.02947605401277542, 0.020061645656824112, -0.01869836263358593, 0.00034572274307720363, -0.0036736230831593275, 0.013076664879918098, 0.05258071422576904, 0.0766717791557312, 0.0005560463760048151, -0.010563013143837452, -0.004511644598096609, 0.05410563200712204, 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560 | 560 | ['Zhen Huang', 'Sabato Marco Siniscalchi', 'I-Fan Chen', 'Jiadong Wu', 'Chin-Hui Lee'] | 1503.02108v2 | We present a Bayesian approach to adapting parameters of a well-trained
context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to
improve automatic speech recognition performance. Given an abundance of DNN
parameters but with only a limited amount of data, the effectiveness of the
adapted DNN model can often be compromised. We formulate maximum a posteriori
(MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an
augmented linear hidden networks connected to the output tied states, or
senones, and compare it to feature space MAP linear regression previously
proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street
Journal task demonstrate the feasibility of the proposed framework. In
supervised adaptation, the proposed MAP adaptation approach provides more than
10% relative error reduction and consistently outperforms the conventional
transformation based methods. Furthermore, we present an initial attempt to
generate hierarchical priors to improve adaptation efficiency and effectiveness
with limited adaptation data by exploiting similarities among senones. | Maximum a Posteriori Adaptation of Network Parameters in Deep Models | 2,015 | http://arxiv.org/pdf/1503.02108v2 | Title Maximum Posteriori Adaptation Network Parameters Deep Models Summary present Bayesian approach adapting parameter welltrained contextdependent deepneuralnetwork hidden Markov model CDDNNHMM improve automatic speech recognition performance Given abundance DNN parameter limited amount data effectiveness adapted DNN model often compromised formulate maximum posteriori MAP adaptation parameter specially designed CDDNNHMM augmented linear hidden network connected output tied state senones compare feature space MAP linear regression previously proposed Experimental evidence 20000word open vocabulary Wall Street Journal task demonstrate feasibility proposed framework supervised adaptation proposed MAP adaptation approach provides 10 relative error reduction consistently outperforms conventional transformation based method Furthermore present initial attempt generate hierarchical prior improve adaptation efficiency effectiveness limited adaptation data exploiting similarity among senones Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.016376910731196404, 0.034700606018304825, -0.0065205697901546955, 0.003025778802111745, 0.021287813782691956, 0.020427532494068146, 0.04004742205142975, -0.012555258348584175, -0.020050641149282455, -0.05148341506719589, -0.06654325127601624, 0.03062780201435089, 0.030081646516919136, 0.010403763502836227, 0.001895442488603294, 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561 | 561 | ['Zhaopeng Tu', 'Baotian Hu', 'Zhengdong Lu', 'Hang Li'] | 1503.02357v2 | We propose a novel method for translation selection in statistical machine
translation, in which a convolutional neural network is employed to judge the
similarity between a phrase pair in two languages. The specifically designed
convolutional architecture encodes not only the semantic similarity of the
translation pair, but also the context containing the phrase in the source
language. Therefore, our approach is able to capture context-dependent semantic
similarities of translation pairs. We adopt a curriculum learning strategy to
train the model: we classify the training examples into easy, medium, and
difficult categories, and gradually build the ability of representing phrase
and sentence level context by using training examples from easy to difficult.
Experimental results show that our approach significantly outperforms the
baseline system by up to 1.4 BLEU points. | Context-Dependent Translation Selection Using Convolutional Neural
Network | 2,015 | http://arxiv.org/pdf/1503.02357v2 | Title ContextDependent Translation Selection Using Convolutional Neural Network Summary propose novel method translation selection statistical machine translation convolutional neural network employed judge similarity phrase pair two language specifically designed convolutional architecture encodes semantic similarity translation pair also context containing phrase source language Therefore approach able capture contextdependent semantic similarity translation pair adopt curriculum learning strategy train model classify training example easy medium difficult category gradually build ability representing phrase sentence level context using training example easy difficult Experimental result show approach significantly outperforms baseline system 14 BLEU point Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.057121142745018005, 0.03913121297955513, -0.013562625274062157, 0.09125379472970963, -0.055765941739082336, 0.009166888892650604, 0.0394693985581398, 0.009660053066909313, -0.05678289011120796, -0.04626738280057907, -0.053807344287633896, 0.005128434393554926, 0.017527494579553604, -0.03251349925994873, 0.009743194095790386, -0.06189746782183647, 0.02292223647236824, 0.03947598859667778, -0.03761816769838333, 0.010058664716780186, -0.010693070478737354, 0.023897940292954445, 0.028649399057030678, 0.04891497641801834, 0.007238875143229961, 0.0064558228477835655, 0.040166545659303665, 0.029224440455436707, 0.039438396692276, -0.026674095541238785, 0.0500066876411438, 0.04123960807919502, 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562 | 562 | ['Baotian Hu', 'Zhengdong Lu', 'Hang Li', 'Qingcai Chen'] | 1503.03244v1 | Semantic matching is of central importance to many natural language tasks
\cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to
adequately model the internal structures of language objects and the
interaction between them. As a step toward this goal, we propose convolutional
neural network models for matching two sentences, by adapting the convolutional
strategy in vision and speech. The proposed models not only nicely represent
the hierarchical structures of sentences with their layer-by-layer composition
and pooling, but also capture the rich matching patterns at different levels.
Our models are rather generic, requiring no prior knowledge on language, and
can hence be applied to matching tasks of different nature and in different
languages. The empirical study on a variety of matching tasks demonstrates the
efficacy of the proposed model on a variety of matching tasks and its
superiority to competitor models. | Convolutional Neural Network Architectures for Matching Natural Language
Sentences | 2,015 | http://arxiv.org/pdf/1503.03244v1 | Title Convolutional Neural Network Architectures Matching Natural Language Sentences Summary Semantic matching central importance many natural language task citebordes2014semanticRetrievalQA successful matching algorithm need adequately model internal structure language object interaction step toward goal propose convolutional neural network model matching two sentence adapting convolutional strategy vision speech proposed model nicely represent hierarchical structure sentence layerbylayer composition pooling also capture rich matching pattern different level model rather generic requiring prior knowledge language hence applied matching task different nature different language empirical study variety matching task demonstrates efficacy proposed model variety matching task superiority competitor model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07127247750759125, 0.04071514308452606, 7.114599429769441e-05, 0.10387472808361053, -0.0757235735654831, -0.0007980315713211894, -0.02864399179816246, 0.0014175277901813388, -0.009861437603831291, -0.039536718279123306, 0.001107489806599915, 0.0004916680045425892, 0.007699381094425917, 0.038788337260484695, 0.008469969965517521, -0.05864640697836876, 0.03446517139673233, 0.0289081409573555, -0.0037556574679911137, 0.04362624138593674, 0.04892148822546005, 0.06434296816587448, 0.0025091979186981916, 0.06955988705158234, 0.0058525120839476585, -0.012229951098561287, 0.01509189885109663, 0.02270575240254402, 0.004336326848715544, 0.00989307276904583, 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563 | 563 | ['Xiaodan Zhu', 'Parinaz Sobhani', 'Hongyu Guo'] | 1503.04881v1 | The chain-structured long short-term memory (LSTM) has showed to be effective
in a wide range of problems such as speech recognition and machine translation.
In this paper, we propose to extend it to tree structures, in which a memory
cell can reflect the history memories of multiple child cells or multiple
descendant cells in a recursive process. We call the model S-LSTM, which
provides a principled way of considering long-distance interaction over
hierarchies, e.g., language or image parse structures. We leverage the models
for semantic composition to understand the meaning of text, a fundamental
problem in natural language understanding, and show that it outperforms a
state-of-the-art recursive model by replacing its composition layers with the
S-LSTM memory blocks. We also show that utilizing the given structures is
helpful in achieving a performance better than that without considering the
structures. | Long Short-Term Memory Over Tree Structures | 2,015 | http://arxiv.org/pdf/1503.04881v1 | Title Long ShortTerm Memory Tree Structures Summary chainstructured long shortterm memory LSTM showed effective wide range problem speech recognition machine translation paper propose extend tree structure memory cell reflect history memory multiple child cell multiple descendant cell recursive process call model SLSTM provides principled way considering longdistance interaction hierarchy eg language image parse structure leverage model semantic composition understand meaning text fundamental problem natural language understanding show outperforms stateoftheart recursive model replacing composition layer SLSTM memory block also show utilizing given structure helpful achieving performance better without considering structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.021890904754400253, -0.0186021588742733, 0.0105922631919384, 0.08318793773651123, -0.003978133667260408, 0.0044142757542431355, -6.259512883843854e-05, -0.010849968530237675, -0.04144003242254257, -0.06341680139303207, -0.040655747056007385, -0.06472526490688324, 0.057167090475559235, 0.030477045103907585, -0.018740426748991013, -0.08175088465213776, 0.030450968071818352, -0.022877046838402748, 0.0011750963749364018, 0.01944289170205593, 0.003844099584966898, -0.014329415746033192, 0.028139488771557808, -0.017467714846134186, 0.00044465536484494805, -0.011570149101316929, -0.042143017053604126, 0.014689908362925053, -0.0028788417112082243, -0.06085814908146858, -0.022819912061095238, 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564 | 564 | ['Krupakar Hans', 'R S Milton'] | 1612.02482v2 | The advent of the attention mechanism in neural machine translation models
has improved the performance of machine translation systems by enabling
selective lookup into the source sentence. In this paper, the efficiencies of
translation using bidirectional encoder attention decoder models were studied
with respect to translation involving morphologically rich languages. The
English - Tamil language pair was selected for this analysis. First, the use of
Word2Vec embedding for both the English and Tamil words improved the
translation results by 0.73 BLEU points over the baseline RNNSearch model with
4.84 BLEU score. The use of morphological segmentation before word
vectorization to split the morphologically rich Tamil words into their
respective morphemes before the translation, caused a reduction in the target
vocabulary size by a factor of 8. Also, this model (RNNMorph) improved the
performance of neural machine translation by 7.05 BLEU points over the
RNNSearch model used over the same corpus. Since the BLEU evaluation of the
RNNMorph model might be unreliable due to an increase in the number of matching
tokens per sentence, the performances of the translations were also compared by
means of human evaluation metrics of adequacy, fluency and relative ranking.
Further, the use of morphological segmentation also improved the efficacy of
the attention mechanism. | Improving the Performance of Neural Machine Translation Involving
Morphologically Rich Languages | 2,016 | http://arxiv.org/pdf/1612.02482v2 | Title Improving Performance Neural Machine Translation Involving Morphologically Rich Languages Summary advent attention mechanism neural machine translation model improved performance machine translation system enabling selective lookup source sentence paper efficiency translation using bidirectional encoder attention decoder model studied respect translation involving morphologically rich language English Tamil language pair selected analysis First use Word2Vec embedding English Tamil word improved translation result 073 BLEU point baseline RNNSearch model 484 BLEU score use morphological segmentation word vectorization split morphologically rich Tamil word respective morpheme translation caused reduction target vocabulary size factor 8 Also model RNNMorph improved performance neural machine translation 705 BLEU point RNNSearch model used corpus Since BLEU evaluation RNNMorph model might unreliable due increase number matching token per sentence performance translation also compared mean human evaluation metric adequacy fluency relative ranking use morphological segmentation also improved efficacy attention mechanism Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06208235025405884, 0.00661317678168416, -0.010848728008568287, 0.06465115398168564, -0.039646659046411514, -0.007100379094481468, -0.01967349275946617, 0.021981865167617798, -0.09096962958574295, -0.04573608934879303, -0.03098827600479126, -0.046243924647569656, 0.05132283642888069, 0.01270466111600399, 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565 | 565 | ['Thomas Laurent', 'James von Brecht'] | 1612.06212v1 | We introduce an exceptionally simple gated recurrent neural network (RNN)
that achieves performance comparable to well-known gated architectures, such as
LSTMs and GRUs, on the word-level language modeling task. We prove that our
model has simple, predicable and non-chaotic dynamics. This stands in stark
contrast to more standard gated architectures, whose underlying dynamical
systems exhibit chaotic behavior. | A recurrent neural network without chaos | 2,016 | http://arxiv.org/pdf/1612.06212v1 | Title recurrent neural network without chaos Summary introduce exceptionally simple gated recurrent neural network RNN achieves performance comparable wellknown gated architecture LSTMs GRUs wordlevel language modeling task prove model simple predicable nonchaotic dynamic stand stark contrast standard gated architecture whose underlying dynamical system exhibit chaotic behavior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.014985080808401108, 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566 | 566 | ['Dimitri Palaz', 'Ronan Collobert', 'Mathew Magimai. -Doss'] | 1312.2137v1 | Most phoneme recognition state-of-the-art systems rely on a classical neural
network classifiers, fed with highly tuned features, such as MFCC or PLP
features. Recent advances in ``deep learning'' approaches questioned such
systems, but while some attempts were made with simpler features such as
spectrograms, state-of-the-art systems still rely on MFCCs. This might be
viewed as a kind of failure from deep learning approaches, which are often
claimed to have the ability to train with raw signals, alleviating the need of
hand-crafted features. In this paper, we investigate a convolutional neural
network approach for raw speech signals. While convolutional architectures got
tremendous success in computer vision or text processing, they seem to have
been let down in the past recent years in the speech processing field. We show
that it is possible to learn an end-to-end phoneme sequence classifier system
directly from raw signal, with similar performance on the TIMIT and WSJ
datasets than existing systems based on MFCC, questioning the need of complex
hand-crafted features on large datasets. | End-to-end Phoneme Sequence Recognition using Convolutional Neural
Networks | 2,013 | http://arxiv.org/pdf/1312.2137v1 | Title Endtoend Phoneme Sequence Recognition using Convolutional Neural Networks Summary phoneme recognition stateoftheart system rely classical neural network classifier fed highly tuned feature MFCC PLP feature Recent advance deep learning approach questioned system attempt made simpler feature spectrogram stateoftheart system still rely MFCCs might viewed kind failure deep learning approach often claimed ability train raw signal alleviating need handcrafted feature paper investigate convolutional neural network approach raw speech signal convolutional architecture got tremendous success computer vision text processing seem let past recent year speech processing field show possible learn endtoend phoneme sequence classifier system directly raw signal similar performance TIMIT WSJ datasets existing system based MFCC questioning need complex handcrafted feature large datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.026019861921668053, 0.06793640553951263, 0.024221213534474373, 0.06060829758644104, 0.027038084343075752, 0.00023603998124599457, 0.04196571558713913, 0.021477218717336655, -0.02096324786543846, 0.010637312196195126, -0.028192345052957535, -0.03161724656820297, 0.03874414414167404, 0.05420498922467232, 0.014173892326653004, -0.0703432485461235, -0.014489108696579933, 0.027534425258636475, 0.027271844446659088, 0.003427951131016016, -0.0032317775767296553, -0.010402685031294823, -0.033477116376161575, 0.07357504218816757, -0.03582974150776863, 0.030432235449552536, -0.00742082204669714, 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567 | 567 | ['Prasanna Kumar Muthukumar', 'Alan W. Black'] | 1409.8558v1 | Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral
coefficients as the vocal tract parameterization of the speech signal. Mel
Cepstral coefficients were never intended to work in a parametric speech
synthesis framework, but as yet, there has been little success in creating a
better parameterization that is more suited to synthesis. In this paper, we use
deep learning algorithms to investigate a data-driven parameterization
technique that is designed for the specific requirements of synthesis. We
create an invertible, low-dimensional, noise-robust encoding of the Mel Log
Spectrum by training a tapered Stacked Denoising Autoencoder (SDA). This SDA is
then unwrapped and used as the initialization for a Multi-Layer Perceptron
(MLP). The MLP is fine-tuned by training it to reconstruct the input at the
output layer. This MLP is then split down the middle to form encoding and
decoding networks. These networks produce a parameterization of the Mel Log
Spectrum that is intended to better fulfill the requirements of synthesis.
Results are reported for experiments conducted using this resulting
parameterization with the ClusterGen speech synthesizer. | A Deep Learning Approach to Data-driven Parameterizations for
Statistical Parametric Speech Synthesis | 2,014 | http://arxiv.org/pdf/1409.8558v1 | Title Deep Learning Approach Datadriven Parameterizations Statistical Parametric Speech Synthesis Summary Nearly Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficient vocal tract parameterization speech signal Mel Cepstral coefficient never intended work parametric speech synthesis framework yet little success creating better parameterization suited synthesis paper use deep learning algorithm investigate datadriven parameterization technique designed specific requirement synthesis create invertible lowdimensional noiserobust encoding Mel Log Spectrum training tapered Stacked Denoising Autoencoder SDA SDA unwrapped used initialization MultiLayer Perceptron MLP MLP finetuned training reconstruct input output layer MLP split middle form encoding decoding network network produce parameterization Mel Log Spectrum intended better fulfill requirement synthesis Results reported experiment conducted using resulting parameterization ClusterGen speech synthesizer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026742789894342422, 0.026720818132162094, 0.012580730952322483, -0.01615588739514351, 0.0337187722325325, -0.043105125427246094, 0.045954830944538116, -0.02139735035598278, -0.08642750978469849, 0.004690163303166628, -0.029437372460961342, -0.00020457124628592283, 0.02073962427675724, 0.057533204555511475, 0.04073010012507439, -0.11935180425643921, -0.016622914001345634, -0.027439523488283157, -0.021220676600933075, -0.003240746445953846, 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568 | 568 | ['Minh-Thang Luong', 'Ilya Sutskever', 'Quoc V. Le', 'Oriol Vinyals', 'Wojciech Zaremba'] | 1410.8206v4 | Neural Machine Translation (NMT) is a new approach to machine translation
that has shown promising results that are comparable to traditional approaches.
A significant weakness in conventional NMT systems is their inability to
correctly translate very rare words: end-to-end NMTs tend to have relatively
small vocabularies with a single unk symbol that represents every possible
out-of-vocabulary (OOV) word. In this paper, we propose and implement an
effective technique to address this problem. We train an NMT system on data
that is augmented by the output of a word alignment algorithm, allowing the NMT
system to emit, for each OOV word in the target sentence, the position of its
corresponding word in the source sentence. This information is later utilized
in a post-processing step that translates every OOV word using a dictionary.
Our experiments on the WMT14 English to French translation task show that this
method provides a substantial improvement of up to 2.8 BLEU points over an
equivalent NMT system that does not use this technique. With 37.5 BLEU points,
our NMT system is the first to surpass the best result achieved on a WMT14
contest task. | Addressing the Rare Word Problem in Neural Machine Translation | 2,014 | http://arxiv.org/pdf/1410.8206v4 | Title Addressing Rare Word Problem Neural Machine Translation Summary Neural Machine Translation NMT new approach machine translation shown promising result comparable traditional approach significant weakness conventional NMT system inability correctly translate rare word endtoend NMTs tend relatively small vocabulary single unk symbol represents every possible outofvocabulary OOV word paper propose implement effective technique address problem train NMT system data augmented output word alignment algorithm allowing NMT system emit OOV word target sentence position corresponding word source sentence information later utilized postprocessing step translates every OOV word using dictionary experiment WMT14 English French translation task show method provides substantial improvement 28 BLEU point equivalent NMT system use technique 375 BLEU point NMT system first surpass best result achieved WMT14 contest task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.022855162620544434, 0.029289616271853447, 0.02399652637541294, 0.04400232434272766, -0.05045294761657715, -0.023506755009293556, -0.010444186627864838, 0.032771993428468704, -0.04892449453473091, -0.015761377289891243, 0.039404843002557755, -0.015806181356310844, 0.03838327154517174, 0.025523344054818153, 0.03211229667067528, -0.040521860122680664, 0.027730703353881836, 0.02993302047252655, -0.006879111751914024, -0.003931284416466951, 0.02453986369073391, 0.009369534440338612, 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569 | 569 | ['Jianpeng Cheng', 'Dimitri Kartsaklis', 'Edward Grefenstette'] | 1411.4116v1 | This paper aims to explore the effect of prior disambiguation on neural
network- based compositional models, with the hope that better semantic
representations for text compounds can be produced. We disambiguate the input
word vectors before they are fed into a compositional deep net. A series of
evaluations shows the positive effect of prior disambiguation for such deep
models. | Investigating the Role of Prior Disambiguation in Deep-learning
Compositional Models of Meaning | 2,014 | http://arxiv.org/pdf/1411.4116v1 | Title Investigating Role Prior Disambiguation Deeplearning Compositional Models Meaning Summary paper aim explore effect prior disambiguation neural network based compositional model hope better semantic representation text compound produced disambiguate input word vector fed compositional deep net series evaluation show positive effect prior disambiguation deep model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05237410217523575, -0.0034892878029495478, -0.017736071720719337, 0.020880402997136116, 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570 | 570 | ['Awni Hannun', 'Carl Case', 'Jared Casper', 'Bryan Catanzaro', 'Greg Diamos', 'Erich Elsen', 'Ryan Prenger', 'Sanjeev Satheesh', 'Shubho Sengupta', 'Adam Coates', 'Andrew Y. Ng'] | 1412.5567v2 | We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. In contrast, our system does not need hand-designed
components to model background noise, reverberation, or speaker variation, but
instead directly learns a function that is robust to such effects. We do not
need a phoneme dictionary, nor even the concept of a "phoneme." Key to our
approach is a well-optimized RNN training system that uses multiple GPUs, as
well as a set of novel data synthesis techniques that allow us to efficiently
obtain a large amount of varied data for training. Our system, called Deep
Speech, outperforms previously published results on the widely studied
Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech
also handles challenging noisy environments better than widely used,
state-of-the-art commercial speech systems. | Deep Speech: Scaling up end-to-end speech recognition | 2,014 | http://arxiv.org/pdf/1412.5567v2 | Title Deep Speech Scaling endtoend speech recognition Summary present stateoftheart speech recognition system developed using endtoend deep learning architecture significantly simpler traditional speech system rely laboriously engineered processing pipeline traditional system also tend perform poorly used noisy environment contrast system need handdesigned component model background noise reverberation speaker variation instead directly learns function robust effect need phoneme dictionary even concept phoneme Key approach welloptimized RNN training system us multiple GPUs well set novel data synthesis technique allow u efficiently obtain large amount varied data training system called Deep Speech outperforms previously published result widely studied Switchboard Hub500 achieving 160 error full test set Deep Speech also handle challenging noisy environment better widely used stateoftheart commercial speech system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.027821414172649384, -0.007224636618047953, 0.012885487638413906, 0.011362634599208832, 0.05090915039181709, -0.010874909348785877, 0.040681254118680954, 0.012056133709847927, -0.026008332148194313, 0.02655702270567417, -0.055612713098526, -0.03361864015460014, 0.03383137658238411, 0.07654588669538498, 0.01008385419845581, -0.0857028067111969, -0.005184985231608152, -0.0007601311663165689, 0.005413442384451628, -0.01581542193889618, 0.06771621108055115, 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571 | 571 | ['Aram Ter-Sarkisov', 'Holger Schwenk', 'Loic Barrault', 'Fethi Bougares'] | 1412.6650v4 | It is today acknowledged that neural network language models outperform
backoff language models in applications like speech recognition or statistical
machine translation. However, training these models on large amounts of data
can take several days. We present efficient techniques to adapt a neural
network language model to new data. Instead of training a completely new model
or relying on mixture approaches, we propose two new methods: continued
training on resampled data or insertion of adaptation layers. We present
experimental results in an CAT environment where the post-edits of professional
translators are used to improve an SMT system. Both methods are very fast and
achieve significant improvements without overfitting the small adaptation data. | Incremental Adaptation Strategies for Neural Network Language Models | 2,014 | http://arxiv.org/pdf/1412.6650v4 | Title Incremental Adaptation Strategies Neural Network Language Models Summary today acknowledged neural network language model outperform backoff language model application like speech recognition statistical machine translation However training model large amount data take several day present efficient technique adapt neural network language model new data Instead training completely new model relying mixture approach propose two new method continued training resampled data insertion adaptation layer present experimental result CAT environment postedits professional translator used improve SMT system method fast achieve significant improvement without overfitting small adaptation data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04433417692780495, 0.026261765509843826, 0.00290094455704093, 0.009626961313188076, 0.0018505413318052888, 0.007606645580381155, 0.011134685017168522, 0.008617978543043137, -0.029768086969852448, -0.0508238859474659, -0.028443163260817528, -0.0031198954675346613, 0.036626119166612625, 0.0071296426467597485, 0.022294579073786736, -0.03378354385495186, 0.03647609055042267, -0.008671138435602188, -0.009152671322226524, -0.006289507262408733, -0.016762813553214073, -0.0012201877543702722, 0.013693868182599545, 0.02944328635931015, 0.019032781943678856, 0.03213484585285187, -0.001862031640484929, 0.016991877928376198, 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572 | 572 | ['Joël Legrand', 'Ronan Collobert'] | 1412.7028v4 | This paper introduces a greedy parser based on neural networks, which
leverages a new compositional sub-tree representation. The greedy parser and
the compositional procedure are jointly trained, and tightly depends on
each-other. The composition procedure outputs a vector representation which
summarizes syntactically (parsing tags) and semantically (words) sub-trees.
Composition and tagging is achieved over continuous (word or tag)
representations, and recurrent neural networks. We reach F1 performance on par
with well-known existing parsers, while having the advantage of speed, thanks
to the greedy nature of the parser. We provide a fully functional
implementation of the method described in this paper. | Joint RNN-Based Greedy Parsing and Word Composition | 2,014 | http://arxiv.org/pdf/1412.7028v4 | Title Joint RNNBased Greedy Parsing Word Composition Summary paper introduces greedy parser based neural network leverage new compositional subtree representation greedy parser compositional procedure jointly trained tightly depends eachother composition procedure output vector representation summarizes syntactically parsing tag semantically word subtrees Composition tagging achieved continuous word tag representation recurrent neural network reach F1 performance par wellknown existing parser advantage speed thanks greedy nature parser provide fully functional implementation method described paper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.038988612592220306, 0.04623155668377876, -0.0028700153343379498, 0.06220453232526779, -0.04495514929294586, -0.0065127890557050705, -0.06172054633498192, -0.009365018457174301, -0.02464917115867138, -0.035406388342380524, 0.005040289834141731, -0.015868930146098137, 0.018734531477093697, 0.02240879274904728, -0.022585883736610413, -0.06536106765270233, 0.034720953553915024, 0.024559784680604935, -0.0026095095090568066, 0.03884531185030937, 0.019763270393013954, 0.013642844744026661, 0.02480754815042019, 0.035502344369888306, -0.054325129836797714, -0.017883099615573883, -0.003041563555598259, -0.014654003083705902, -0.03938774764537811, -0.032458215951919556, 0.044645316898822784, -0.004437378607690334, 0.0005539968842640519, -0.0015808219322934747, 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573 | 573 | ['Pascal Vincent', 'Alexandre de Brébisson', 'Xavier Bouthillier'] | 1412.7091v3 | An important class of problems involves training deep neural networks with
sparse prediction targets of very high dimension D. These occur naturally in
e.g. neural language models or the learning of word-embeddings, often posed as
predicting the probability of next words among a vocabulary of size D (e.g. 200
000). Computing the equally large, but typically non-sparse D-dimensional
output vector from a last hidden layer of reasonable dimension d (e.g. 500)
incurs a prohibitive O(Dd) computational cost for each example, as does
updating the D x d output weight matrix and computing the gradient needed for
backpropagation to previous layers. While efficient handling of large sparse
network inputs is trivial, the case of large sparse targets is not, and has
thus so far been sidestepped with approximate alternatives such as hierarchical
softmax or sampling-based approximations during training. In this work we
develop an original algorithmic approach which, for a family of loss functions
that includes squared error and spherical softmax, can compute the exact loss,
gradient update for the output weights, and gradient for backpropagation, all
in O(d^2) per example instead of O(Dd), remarkably without ever computing the
D-dimensional output. The proposed algorithm yields a speedup of D/4d , i.e.
two orders of magnitude for typical sizes, for that critical part of the
computations that often dominates the training time in this kind of network
architecture. | Efficient Exact Gradient Update for training Deep Networks with Very
Large Sparse Targets | 2,014 | http://arxiv.org/pdf/1412.7091v3 | Title Efficient Exact Gradient Update training Deep Networks Large Sparse Targets Summary important class problem involves training deep neural network sparse prediction target high dimension occur naturally eg neural language model learning wordembeddings often posed predicting probability next word among vocabulary size eg 200 000 Computing equally large typically nonsparse Ddimensional output vector last hidden layer reasonable dimension eg 500 incurs prohibitive ODd computational cost example updating x output weight matrix computing gradient needed backpropagation previous layer efficient handling large sparse network input trivial case large sparse target thus far sidestepped approximate alternative hierarchical softmax samplingbased approximation training work develop original algorithmic approach family loss function includes squared error spherical softmax compute exact loss gradient update output weight gradient backpropagation Od2 per example instead ODd remarkably without ever computing Ddimensional output proposed algorithm yield speedup D4d ie two order magnitude typical size critical part computation often dominates training time kind network architecture Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02790750376880169, 0.03701736778020859, 0.024033328518271446, 0.035924483090639114, 0.025002453476190567, -0.016908086836338043, -0.024662375450134277, 0.01325901784002781, -0.06803694367408752, -0.04705901816487312, -0.037853434681892395, -0.03654506430029869, 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574 | 574 | ['Lili Mou', 'Hao Peng', 'Ge Li', 'Yan Xu', 'Lu Zhang', 'Zhi Jin'] | 1504.01106v5 | This paper proposes a tree-based convolutional neural network (TBCNN) for
discriminative sentence modeling. Our models leverage either constituency trees
or dependency trees of sentences. The tree-based convolution process extracts
sentences' structural features, and these features are aggregated by max
pooling. Such architecture allows short propagation paths between the output
layer and underlying feature detectors, which enables effective structural
feature learning and extraction. We evaluate our models on two tasks: sentiment
analysis and question classification. In both experiments, TBCNN outperforms
previous state-of-the-art results, including existing neural networks and
dedicated feature/rule engineering. We also make efforts to visualize the
tree-based convolution process, shedding light on how our models work. | Discriminative Neural Sentence Modeling by Tree-Based Convolution | 2,015 | http://arxiv.org/pdf/1504.01106v5 | Title Discriminative Neural Sentence Modeling TreeBased Convolution Summary paper proposes treebased convolutional neural network TBCNN discriminative sentence modeling model leverage either constituency tree dependency tree sentence treebased convolution process extract sentence structural feature feature aggregated max pooling architecture allows short propagation path output layer underlying feature detector enables effective structural feature learning extraction evaluate model two task sentiment analysis question classification experiment TBCNN outperforms previous stateoftheart result including existing neural network dedicated featurerule engineering also make effort visualize treebased convolution process shedding light model work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05543934553861618, 0.02635028585791588, 0.011994539760053158, 0.09633726626634598, -0.045005373656749725, 0.015982534736394882, -0.0516539067029953, -0.03248940780758858, -0.03221478313207626, -0.07559776306152344, 1.281684603782196e-06, -0.01715259812772274, -0.030680643394589424, 0.04130610078573227, -0.03602752834558487, -0.04713580384850502, 0.01857433095574379, 0.04672564938664436, 0.06692206859588623, 0.007817601785063744, 0.033187590539455414, 0.03754819184541702, 0.014978361316025257, 0.023961862549185753, -0.049155548214912415, -0.05616040155291557, 0.0016457787714898586, 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575 | 575 | ['Han Zhao', 'Zhengdong Lu', 'Pascal Poupart'] | 1504.05070v2 | The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets. | Self-Adaptive Hierarchical Sentence Model | 2,015 | http://arxiv.org/pdf/1504.05070v2 | Title SelfAdaptive Hierarchical Sentence Model Summary ability accurately model sentence varying stage eg wordphrasesentence play central role natural language processing effort towards goal propose selfadaptive hierarchical sentence model AdaSent AdaSent effectively form hierarchy representation word phrase sentence recursive gated local composition adjacent segment design competitive mechanism gating network allow representation sentence engaged particular learning task eg classification therefore effectively mitigating gradient vanishing problem persistent recursive model qualitative quantitative analysis show AdaSent automatically form select representation suitable task hand training yielding superior classification performance competitor model 5 benchmark data set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05538245290517807, 0.018063362687826157, -0.032322634011507034, 0.04892224818468094, -0.05491231381893158, 0.003983187954872847, -0.0020803736988455057, -0.04065811634063721, 0.03117627464234829, -0.08946610242128372, -0.021120086312294006, 0.03724454343318939, -0.035660646855831146, 0.05444278568029404, 0.0011158548295497894, -0.06466632336378098, 0.04118647798895836, -0.020944593474268913, 0.025657180696725845, 0.018472464755177498, 0.028474753722548485, 0.007767002563923597, 0.002473704284057021, 0.03450727090239525, 0.006575651932507753, -0.050587672740221024, -0.018210144713521004, -0.017314404249191284, 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576 | 576 | ['Cicero Nogueira dos Santos', 'Bing Xiang', 'Bowen Zhou'] | 1504.06580v2 | Relation classification is an important semantic processing task for which
state-ofthe-art systems still rely on costly handcrafted features. In this work
we tackle the relation classification task using a convolutional neural network
that performs classification by ranking (CR-CNN). We propose a new pairwise
ranking loss function that makes it easy to reduce the impact of artificial
classes. We perform experiments using the the SemEval-2010 Task 8 dataset,
which is designed for the task of classifying the relationship between two
nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art
for this dataset and achieve a F1 of 84.1 without using any costly handcrafted
features. Additionally, our experimental results show that: (1) our approach is
more effective than CNN followed by a softmax classifier; (2) omitting the
representation of the artificial class Other improves both precision and
recall; and (3) using only word embeddings as input features is enough to
achieve state-of-the-art results if we consider only the text between the two
target nominals. | Classifying Relations by Ranking with Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1504.06580v2 | Title Classifying Relations Ranking Convolutional Neural Networks Summary Relation classification important semantic processing task stateoftheart system still rely costly handcrafted feature work tackle relation classification task using convolutional neural network performs classification ranking CRCNN propose new pairwise ranking loss function make easy reduce impact artificial class perform experiment using SemEval2010 Task 8 dataset designed task classifying relationship two nominal marked sentence Using CRCNN outperform stateoftheart dataset achieve F1 841 without using costly handcrafted feature Additionally experimental result show 1 approach effective CNN followed softmax classifier 2 omitting representation artificial class improves precision recall 3 using word embeddings input feature enough achieve stateoftheart result consider text two target nominal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07756244391202927, 0.04017649590969086, 0.007496031932532787, 0.0741201713681221, 0.000742192380130291, 0.008697119541466236, -0.0011894071940332651, 0.020560026168823242, 0.00620815996080637, -0.057921066880226135, -0.027497971430420876, 0.00218995101749897, -0.005068772938102484, -0.008564497344195843, -0.0205851923674345, -0.04346698150038719, 0.0309641994535923, 0.06563657522201538, 0.03562447801232338, 0.006890448275953531, -0.00439069839194417, 0.04357904568314552, 0.010671483352780342, 0.027595845982432365, 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577 | 577 | ['Thanh-Le Ha', 'Jan Niehues', 'Alex Waibel'] | 1504.07395v1 | In this paper we combine the advantages of a model using global source
sentence contexts, the Discriminative Word Lexicon, and neural networks. By
using deep neural networks instead of the linear maximum entropy model in the
Discriminative Word Lexicon models, we are able to leverage dependencies
between different source words due to the non-linearity. Furthermore, the
models for different target words can share parameters and therefore data
sparsity problems are effectively reduced.
By using this approach in a state-of-the-art translation system, we can
improve the performance by up to 0.5 BLEU points for three different language
pairs on the TED translation task. | Lexical Translation Model Using a Deep Neural Network Architecture | 2,015 | http://arxiv.org/pdf/1504.07395v1 | Title Lexical Translation Model Using Deep Neural Network Architecture Summary paper combine advantage model using global source sentence context Discriminative Word Lexicon neural network using deep neural network instead linear maximum entropy model Discriminative Word Lexicon model able leverage dependency different source word due nonlinearity Furthermore model different target word share parameter therefore data sparsity problem effectively reduced using approach stateoftheart translation system improve performance 05 BLEU point three different language pair TED translation task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 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578 | 578 | ['Andrej Karpathy', 'Justin Johnson', 'Li Fei-Fei'] | 1506.02078v2 | Recurrent Neural Networks (RNNs), and specifically a variant with Long
Short-Term Memory (LSTM), are enjoying renewed interest as a result of
successful applications in a wide range of machine learning problems that
involve sequential data. However, while LSTMs provide exceptional results in
practice, the source of their performance and their limitations remain rather
poorly understood. Using character-level language models as an interpretable
testbed, we aim to bridge this gap by providing an analysis of their
representations, predictions and error types. In particular, our experiments
reveal the existence of interpretable cells that keep track of long-range
dependencies such as line lengths, quotes and brackets. Moreover, our
comparative analysis with finite horizon n-gram models traces the source of the
LSTM improvements to long-range structural dependencies. Finally, we provide
analysis of the remaining errors and suggests areas for further study. | Visualizing and Understanding Recurrent Networks | 2,015 | http://arxiv.org/pdf/1506.02078v2 | Title Visualizing Understanding Recurrent Networks Summary Recurrent Neural Networks RNNs specifically variant Long ShortTerm Memory LSTM enjoying renewed interest result successful application wide range machine learning problem involve sequential data However LSTMs provide exceptional result practice source performance limitation remain rather poorly understood Using characterlevel language model interpretable testbed aim bridge gap providing analysis representation prediction error type particular experiment reveal existence interpretable cell keep track longrange dependency line length quote bracket Moreover comparative analysis finite horizon ngram model trace source LSTM improvement longrange structural dependency Finally provide analysis remaining error suggests area study Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.011355806142091751, -0.005041065160185099, -0.01834714412689209, 0.030946750193834305, -0.02056630700826645, -0.010613009333610535, 0.018238291144371033, -0.003978530876338482, -0.042686380445957184, 0.011484013870358467, -0.0167690422385931, -0.05414002388715744, 0.057105064392089844, 0.05567161366343498, 0.024605724960565567, -0.06387881189584732, 0.02339831367135048, -0.015174875035881996, -0.014519678428769112, 0.006709251087158918, 0.005088239908218384, -0.038227006793022156, -0.017088398337364197, 0.06328067183494568, -0.013770775869488716, -0.021523475646972656, 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579 | 579 | ['Cheng-Tao Chung', 'Cheng-Yu Tsai', 'Hsiang-Hung Lu', 'Yuan-ming Liou', 'Yen-Chen Wu', 'Yen-Ju Lu', 'Hung-yi Lee', 'Lin-shan Lee'] | 1506.02327v1 | This paper summarizes the work done by the authors for the Zero Resource
Speech Challenge organized in the technical program of Interspeech 2015. The
goal of the challenge is to discover linguistic units directly from unlabeled
speech data. The Multi-layered Acoustic Tokenizer (MAT) proposed in this work
automatically discovers multiple sets of acoustic tokens from the given corpus.
Each acoustic token set is specified by a set of hyperparameters that describe
the model configuration. These sets of acoustic tokens carry different
characteristics of the given corpus and the language behind thus can be
mutually reinforced. The multiple sets of token labels are then used as the
targets of a Multi-target DNN (MDNN) trained on low-level acoustic features.
Bottleneck features extracted from the MDNN are used as feedback for the MAT
and the MDNN itself. We call this iterative system the Multi-layered Acoustic
Tokenizing Deep Neural Network (MAT-DNN) which generates high quality features
for track 1 of the challenge and acoustic tokens for track 2 of the challenge. | A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for
Unsupervised Discovery of Linguistic Units and Generation of High Quality
Features | 2,015 | http://arxiv.org/pdf/1506.02327v1 | Title Multilayered Acoustic Tokenizing Deep Neural Network MATDNN Unsupervised Discovery Linguistic Units Generation High Quality Features Summary paper summarizes work done author Zero Resource Speech Challenge organized technical program Interspeech 2015 goal challenge discover linguistic unit directly unlabeled speech data Multilayered Acoustic Tokenizer MAT proposed work automatically discovers multiple set acoustic token given corpus acoustic token set specified set hyperparameters describe model configuration set acoustic token carry different characteristic given corpus language behind thus mutually reinforced multiple set token label used target Multitarget DNN MDNN trained lowlevel acoustic feature Bottleneck feature extracted MDNN used feedback MAT MDNN call iterative system Multilayered Acoustic Tokenizing Deep Neural Network MATDNN generates high quality feature track 1 challenge acoustic token track 2 challenge Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.023208608850836754, 0.03669661656022072, 0.011429867707192898, 0.01780596747994423, -0.01536133885383606, -0.038021013140678406, 0.07236523181200027, -0.019846657291054726, -0.10259981453418732, -0.01436596643179655, -0.0335264652967453, 0.04753900691866875, -0.011864783242344856, 0.043359726667404175, 0.029625006020069122, -0.0399046391248703, 0.026072775945067406, -0.024332650005817413, 0.052128665149211884, -0.011887573637068272, 0.009746340103447437, 0.0037806774489581585, -0.027873283252120018, 0.09098754823207855, 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580 | 580 | ['Douglas Bagnall'] | 1506.04891v2 | Recurrent neural networks (RNNs) are very good at modelling the flow of text,
but typically need to be trained on a far larger corpus than is available for
the PAN 2015 Author Identification task. This paper describes a novel approach
where the output layer of a character-level RNN language model is split into
several independent predictive sub-models, each representing an author, while
the recurrent layer is shared by all. This allows the recurrent layer to model
the language as a whole without over-fitting, while the outputs select aspects
of the underlying model that reflect their author's style. The method proves
competitive, ranking first in two of the four languages. | Author Identification using Multi-headed Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1506.04891v2 | Title Author Identification using Multiheaded Recurrent Neural Networks Summary Recurrent neural network RNNs good modelling flow text typically need trained far larger corpus available PAN 2015 Author Identification task paper describes novel approach output layer characterlevel RNN language model split several independent predictive submodels representing author recurrent layer shared allows recurrent layer model language whole without overfitting output select aspect underlying model reflect author style method prof competitive ranking first two four language Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 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581 | 581 | ['Fandong Meng', 'Zhengdong Lu', 'Zhaopeng Tu', 'Hang Li', 'Qun Liu'] | 1506.06442v4 | We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence
learning, which performs the task through a series of nonlinear transformations
from the representation of the input sequence (e.g., a Chinese sentence) to the
final output sequence (e.g., translation to English). Inspired by the recently
proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate
representations in stacked layers of memories, and use read-write operations on
the memories to realize the nonlinear transformations between the
representations. The types of transformations are designed in advance but the
parameters are learned from data. Through layer-by-layer transformations,
DEEPMEMORY can model complicated relations between sequences necessary for
applications such as machine translation between distant languages. The
architecture can be trained with normal back-propagation on sequenceto-sequence
data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is
broad enough to subsume the state-of-the-art neural translation model in
(Bahdanau et al., 2015) as its special case, while significantly improving upon
the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely
neural network-based, can achieve performance comparable to the traditional
phrase-based machine translation system Moses with a small vocabulary and a
modest parameter size. | A Deep Memory-based Architecture for Sequence-to-Sequence Learning | 2,015 | http://arxiv.org/pdf/1506.06442v4 | Title Deep Memorybased Architecture SequencetoSequence Learning Summary propose DEEPMEMORY novel deep architecture sequencetosequence learning performs task series nonlinear transformation representation input sequence eg Chinese sentence final output sequence eg translation English Inspired recently proposed Neural Turing Machine Graves et al 2014 store intermediate representation stacked layer memory use readwrite operation memory realize nonlinear transformation representation type transformation designed advance parameter learned data layerbylayer transformation DEEPMEMORY model complicated relation sequence necessary application machine translation distant language architecture trained normal backpropagation sequencetosequence data learning easily scaled large corpus DEEPMEMORY broad enough subsume stateoftheart neural translation model Bahdanau et al 2015 special case significantly improving upon model deeper architecture Remarkably DEEPMEMORY purely neural networkbased achieve performance comparable traditional phrasebased machine translation system Moses small vocabulary modest parameter size Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.028884893283247948, 0.06040644273161888, -0.02255413681268692, 0.030304238200187683, -0.01584515906870365, 0.012662301771342754, 0.010223411954939365, -0.003853060305118561, -0.012946471571922302, -0.026064522564411163, -0.023150455206632614, -0.04285856708884239, 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582 | 582 | ['Ankit Kumar', 'Ozan Irsoy', 'Peter Ondruska', 'Mohit Iyyer', 'James Bradbury', 'Ishaan Gulrajani', 'Victor Zhong', 'Romain Paulus', 'Richard Socher'] | 1506.07285v5 | Most tasks in natural language processing can be cast into question answering
(QA) problems over language input. We introduce the dynamic memory network
(DMN), a neural network architecture which processes input sequences and
questions, forms episodic memories, and generates relevant answers. Questions
trigger an iterative attention process which allows the model to condition its
attention on the inputs and the result of previous iterations. These results
are then reasoned over in a hierarchical recurrent sequence model to generate
answers. The DMN can be trained end-to-end and obtains state-of-the-art results
on several types of tasks and datasets: question answering (Facebook's bAbI
dataset), text classification for sentiment analysis (Stanford Sentiment
Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The
training for these different tasks relies exclusively on trained word vector
representations and input-question-answer triplets. | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | 2,015 | http://arxiv.org/pdf/1506.07285v5 | Title Ask Anything Dynamic Memory Networks Natural Language Processing Summary task natural language processing cast question answering QA problem language input introduce dynamic memory network DMN neural network architecture process input sequence question form episodic memory generates relevant answer Questions trigger iterative attention process allows model condition attention input result previous iteration result reasoned hierarchical recurrent sequence model generate answer DMN trained endtoend obtains stateoftheart result several type task datasets question answering Facebooks bAbI dataset text classification sentiment analysis Stanford Sentiment Treebank sequence modeling partofspeech tagging WSJPTB training different task relies exclusively trained word vector representation inputquestionanswer triplet Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05158541724085808, 0.02812228724360466, -0.02276724763214588, 0.02513764426112175, -0.013104015029966831, 0.012974195182323456, 0.020096927881240845, 0.005087028257548809, 0.0018260703654959798, -0.05064588785171509, 0.012586611323058605, -0.014207405969500542, -0.007919731549918652, 0.08814384788274765, 0.022306222468614578, -0.05672716721892357, 0.06019505858421326, 0.003386727999895811, 0.007412215229123831, 0.012529314495623112, 0.009813901968300343, 0.017039375379681587, -0.022455228492617607, 0.009103869087994099, 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583 | 583 | ['Lantian Li', 'Yiye Lin', 'Zhiyong Zhang', 'Dong Wang'] | 1506.08349v1 | A deep learning approach has been proposed recently to derive speaker
identifies (d-vector) by a deep neural network (DNN). This approach has been
applied to text-dependent speaker recognition tasks and shows reasonable
performance gains when combined with the conventional i-vector approach.
Although promising, the existing d-vector implementation still can not compete
with the i-vector baseline. This paper presents two improvements for the deep
learning approach: a phonedependent DNN structure to normalize phone variation,
and a new scoring approach based on dynamic time warping (DTW). Experiments on
a text-dependent speaker recognition task demonstrated that the proposed
methods can provide considerable performance improvement over the existing
d-vector implementation. | Improved Deep Speaker Feature Learning for Text-Dependent Speaker
Recognition | 2,015 | http://arxiv.org/pdf/1506.08349v1 | Title Improved Deep Speaker Feature Learning TextDependent Speaker Recognition Summary deep learning approach proposed recently derive speaker identifies dvector deep neural network DNN approach applied textdependent speaker recognition task show reasonable performance gain combined conventional ivector approach Although promising existing dvector implementation still compete ivector baseline paper present two improvement deep learning approach phonedependent DNN structure normalize phone variation new scoring approach based dynamic time warping DTW Experiments textdependent speaker recognition task demonstrated proposed method provide considerable performance improvement existing dvector implementation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.002774872351437807, 0.037153977900743484, -0.0013352383393794298, 0.021412957459688187, -0.04566184803843498, 0.02175619639456272, 0.05310129374265671, -0.01793704181909561, -0.021418150514364243, -0.01175806112587452, -0.0002018620871240273, -0.016237840056419373, 0.04732605814933777, -0.021833205595612526, -0.029306044802069664, -0.05123240873217583, -0.017852775752544403, 0.0418621301651001, 0.0187847800552845, -0.004552207887172699, 0.02848624438047409, 0.002268756739795208, -0.05264798179268837, -0.003133632941171527, -0.0655277669429779, 0.0037037949077785015, 0.007084456272423267, -0.01122488547116518, 0.06373777985572815, 0.016631534323096275, 0.008818581700325012, 0.024618031457066536, -0.0027442551217973232, 0.04232752323150635, 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584 | 584 | ['Nal Kalchbrenner', 'Ivo Danihelka', 'Alex Graves'] | 1507.01526v3 | This paper introduces Grid Long Short-Term Memory, a network of LSTM cells
arranged in a multidimensional grid that can be applied to vectors, sequences
or higher dimensional data such as images. The network differs from existing
deep LSTM architectures in that the cells are connected between network layers
as well as along the spatiotemporal dimensions of the data. The network
provides a unified way of using LSTM for both deep and sequential computation.
We apply the model to algorithmic tasks such as 15-digit integer addition and
sequence memorization, where it is able to significantly outperform the
standard LSTM. We then give results for two empirical tasks. We find that 2D
Grid LSTM achieves 1.47 bits per character on the Wikipedia character
prediction benchmark, which is state-of-the-art among neural approaches. In
addition, we use the Grid LSTM to define a novel two-dimensional translation
model, the Reencoder, and show that it outperforms a phrase-based reference
system on a Chinese-to-English translation task. | Grid Long Short-Term Memory | 2,015 | http://arxiv.org/pdf/1507.01526v3 | Title Grid Long ShortTerm Memory Summary paper introduces Grid Long ShortTerm Memory network LSTM cell arranged multidimensional grid applied vector sequence higher dimensional data image network differs existing deep LSTM architecture cell connected network layer well along spatiotemporal dimension data network provides unified way using LSTM deep sequential computation apply model algorithmic task 15digit integer addition sequence memorization able significantly outperform standard LSTM give result two empirical task find 2D Grid LSTM achieves 147 bit per character Wikipedia character prediction benchmark stateoftheart among neural approach addition use Grid LSTM define novel twodimensional translation model Reencoder show outperforms phrasebased reference system ChinesetoEnglish translation task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0004072290612384677, 0.03275850787758827, -0.0063985129818320274, 0.07208779454231262, 0.00855559017509222, 0.025971632450819016, 0.046660978347063065, -0.023915085941553116, -0.013846147805452347, -0.008136903867125511, -0.03842177987098694, -0.10288789123296738, 0.04769172519445419, 0.04459302872419357, 0.04737400636076927, -0.0576179139316082, -0.0010369597002863884, 6.658892380073667e-05, -0.027555957436561584, 0.00723298592492938, 0.011227278970181942, -0.02019597962498665, -0.007480907253921032, 0.002320192288607359, -0.021376879885792732, -0.006758588366210461, 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585 | 585 | ['Yang Liu', 'Furu Wei', 'Sujian Li', 'Heng Ji', 'Ming Zhou', 'Houfeng Wang'] | 1507.04646v1 | Previous research on relation classification has verified the effectiveness
of using dependency shortest paths or subtrees. In this paper, we further
explore how to make full use of the combination of these dependency
information. We first propose a new structure, termed augmented dependency path
(ADP), which is composed of the shortest dependency path between two entities
and the subtrees attached to the shortest path. To exploit the semantic
representation behind the ADP structure, we develop dependency-based neural
networks (DepNN): a recursive neural network designed to model the subtrees,
and a convolutional neural network to capture the most important features on
the shortest path. Experiments on the SemEval-2010 dataset show that our
proposed method achieves state-of-art results. | A Dependency-Based Neural Network for Relation Classification | 2,015 | http://arxiv.org/pdf/1507.04646v1 | Title DependencyBased Neural Network Relation Classification Summary Previous research relation classification verified effectiveness using dependency shortest path subtrees paper explore make full use combination dependency information first propose new structure termed augmented dependency path ADP composed shortest dependency path two entity subtrees attached shortest path exploit semantic representation behind ADP structure develop dependencybased neural network DepNN recursive neural network designed model subtrees convolutional neural network capture important feature shortest path Experiments SemEval2010 dataset show proposed method achieves stateofart result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03561617434024811, 0.046365831047296524, -0.0013076739851385355, 0.07303697615861893, -0.00864286720752716, -0.01818736642599106, -0.06447212398052216, 0.028535889461636543, 0.008817428722977638, -0.04436936601996422, 0.0034797396510839462, 0.02065291255712509, 0.01121324859559536, -0.013998960144817829, -0.0286827702075243, -0.07426954060792923, 0.019016021862626076, 0.06294254213571548, -0.027771826833486557, 0.0202175322920084, 0.013669264502823353, 0.0340239442884922, -0.0019243108108639717, 0.018900174647569656, -0.08390581607818604, -0.05203985795378685, -0.008586837910115719, 0.020652638748288155, 0.0059095872566103935, -0.06874959915876389, 0.04262010380625725, 0.03860660642385483, 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586 | 586 | ['Jian Tang', 'Meng Qu', 'Qiaozhu Mei'] | 1508.00200v1 | Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune. | PTE: Predictive Text Embedding through Large-scale Heterogeneous Text
Networks | 2,015 | http://arxiv.org/pdf/1508.00200v1 | Title PTE Predictive Text Embedding Largescale Heterogeneous Text Networks Summary Unsupervised text embedding method Skipgram Paragraph Vector attracting increasing attention due simplicity scalability effectiveness However comparing sophisticated deep learning architecture convolutional neural network method usually yield inferior result applied particular machine learning task One possible reason text embedding method learn representation text fully unsupervised way without leveraging labeled information available task Although low dimensional representation learned applicable many different task particularly tuned task paper fill gap proposing semisupervised representation learning method text data call textitpredictive text embedding PTE Predictive text embedding utilizes labeled unlabeled data learn embedding text labeled information different level word cooccurrence information first represented largescale heterogeneous text network embedded low dimensional space principled efficient algorithm low dimensional embedding preserve semantic closeness word document also strong predictive power particular task Compared recent supervised approach based convolutional neural network predictive text embedding comparable effective much efficient fewer parameter tune Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010130133479833603, -0.006559894885867834, 0.008196325041353703, 0.08284035325050354, -0.006328774616122246, -0.007417625281959772, 0.03629646450281143, 0.010787906125187874, 0.05100501701235771, 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587 | 587 | ['Dongxu Zhang', 'Dong Wang'] | 1508.01006v2 | Deep learning has gained much success in sentence-level relation
classification. For example, convolutional neural networks (CNN) have delivered
competitive performance without much effort on feature engineering as the
conventional pattern-based methods. Thus a lot of works have been produced
based on CNN structures. However, a key issue that has not been well addressed
by the CNN-based method is the lack of capability to learn temporal features,
especially long-distance dependency between nominal pairs. In this paper, we
propose a simple framework based on recurrent neural networks (RNN) and compare
it with CNN-based model. To show the limitation of popular used SemEval-2010
Task 8 dataset, we introduce another dataset refined from MIMLRE(Angeli et al.,
2014). Experiments on two different datasets strongly indicates that the
RNN-based model can deliver better performance on relation classification, and
it is particularly capable of learning long-distance relation patterns. This
makes it suitable for real-world applications where complicated expressions are
often involved. | Relation Classification via Recurrent Neural Network | 2,015 | http://arxiv.org/pdf/1508.01006v2 | Title Relation Classification via Recurrent Neural Network Summary Deep learning gained much success sentencelevel relation classification example convolutional neural network CNN delivered competitive performance without much effort feature engineering conventional patternbased method Thus lot work produced based CNN structure However key issue well addressed CNNbased method lack capability learn temporal feature especially longdistance dependency nominal pair paper propose simple framework based recurrent neural network RNN compare CNNbased model show limitation popular used SemEval2010 Task 8 dataset introduce another dataset refined MIMLREAngeli et al 2014 Experiments two different datasets strongly indicates RNNbased model deliver better performance relation classification particularly capable learning longdistance relation pattern make suitable realworld application complicated expression often involved Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06687229871749878, 0.04454890638589859, -0.002900527324527502, 0.08168329298496246, -0.041271060705184937, -0.015379820019006729, -0.004754744004458189, 0.02586425095796585, -0.014359858818352222, -0.06835654377937317, 0.007101353257894516, -0.036215901374816895, 0.01686316914856434, 0.00855633057653904, -0.033260736614465714, -0.056211572140455246, 0.013692029751837254, 0.04716359078884125, 0.015616768039762974, 0.02569148689508438, -0.01210167445242405, 0.03391966596245766, 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588 | 588 | ['Dongxu Zhang', 'Tianyi Luo', 'Dong Wang', 'Rong Liu'] | 1508.01011v1 | Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian
model for topic inference. In spite of its great success, inferring the latent
topic distribution with LDA is time-consuming. Motivated by the transfer
learning approach proposed by~\newcite{hinton2015distilling}, we present a
novel method that uses LDA to supervise the training of a deep neural network
(DNN), so that the DNN can approximate the costly LDA inference with less
computation. Our experiments on a document classification task show that a
simple DNN can learn the LDA behavior pretty well, while the inference is
speeded up tens or hundreds of times. | Learning from LDA using Deep Neural Networks | 2,015 | http://arxiv.org/pdf/1508.01011v1 | Title Learning LDA using Deep Neural Networks Summary Latent Dirichlet Allocation LDA threelevel hierarchical Bayesian model topic inference spite great success inferring latent topic distribution LDA timeconsuming Motivated transfer learning approach proposed bynewcitehinton2015distilling present novel method us LDA supervise training deep neural network DNN DNN approximate costly LDA inference le computation experiment document classification task show simple DNN learn LDA behavior pretty well inference speeded ten hundred time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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589 | 589 | ['Marek Rei'] | 1508.03854v1 | We investigate an extension of continuous online learning in recurrent neural
network language models. The model keeps a separate vector representation of
the current unit of text being processed and adaptively adjusts it after each
prediction. The initial experiments give promising results, indicating that the
method is able to increase language modelling accuracy, while also decreasing
the parameters needed to store the model along with the computation required at
each step. | Online Representation Learning in Recurrent Neural Language Models | 2,015 | http://arxiv.org/pdf/1508.03854v1 | Title Online Representation Learning Recurrent Neural Language Models Summary investigate extension continuous online learning recurrent neural network language model model keep separate vector representation current unit text processed adaptively adjusts prediction initial experiment give promising result indicating method able increase language modelling accuracy also decreasing parameter needed store model along computation required step Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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590 | 590 | ['Ye Zhang', 'Byron Wallace'] | 1510.03820v4 | Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim
2014, kalchbrenner 2014, johnson 2014). However, these models require
practitioners to specify an exact model architecture and set accompanying
hyperparameters, including the filter region size, regularization parameters,
and so on. It is currently unknown how sensitive model performance is to
changes in these configurations for the task of sentence classification. We
thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of
architecture components on model performance; our aim is to distinguish between
important and comparatively inconsequential design decisions for sentence
classification. We focus on one-layer CNNs (to the exclusion of more complex
models) due to their comparative simplicity and strong empirical performance,
which makes it a modern standard baseline method akin to Support Vector Machine
(SVMs) and logistic regression. We derive practical advice from our extensive
empirical results for those interested in getting the most out of CNNs for
sentence classification in real world settings. | A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional
Neural Networks for Sentence Classification | 2,015 | http://arxiv.org/pdf/1510.03820v4 | Title Sensitivity Analysis Practitioners Guide Convolutional Neural Networks Sentence Classification Summary Convolutional Neural Networks CNNs recently achieved remarkably strong performance practically important task sentence classification kim 2014 kalchbrenner 2014 johnson 2014 However model require practitioner specify exact model architecture set accompanying hyperparameters including filter region size regularization parameter currently unknown sensitive model performance change configuration task sentence classification thus conduct sensitivity analysis onelayer CNNs explore effect architecture component model performance aim distinguish important comparatively inconsequential design decision sentence classification focus onelayer CNNs exclusion complex model due comparative simplicity strong empirical performance make modern standard baseline method akin Support Vector Machine SVMs logistic regression derive practical advice extensive empirical result interested getting CNNs sentence classification real world setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07863238453865051, 0.04088165611028671, -0.023126494139432907, 0.05393735319375992, -0.03360119089484215, 0.0050298250280320644, 0.028157170861959457, 0.02379668690264225, -0.020739585161209106, -0.06305491179227829, -0.00040149083361029625, -0.00851241685450077, -0.009140674024820328, 0.011043904349207878, -0.009641474112868309, -0.04543013870716095, 0.043639082461595535, 0.07156877964735031, 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591 | 591 | ['Yu Zhang', 'Ekapol Chuangsuwanich', 'James Glass', 'Dong Yu'] | 1510.08985v1 | In this paper, we investigate the use of prediction-adaptation-correction
recurrent neural networks (PAC-RNNs) for low-resource speech recognition. A
PAC-RNN is comprised of a pair of neural networks in which a {\it correction}
network uses auxiliary information given by a {\it prediction} network to help
estimate the state probability. The information from the correction network is
also used by the prediction network in a recurrent loop. Our model outperforms
other state-of-the-art neural networks (DNNs, LSTMs) on IARPA-Babel tasks.
Moreover, transfer learning from a language that is similar to the target
language can help improve performance further. | Prediction-Adaptation-Correction Recurrent Neural Networks for
Low-Resource Language Speech Recognition | 2,015 | http://arxiv.org/pdf/1510.08985v1 | Title PredictionAdaptationCorrection Recurrent Neural Networks LowResource Language Speech Recognition Summary paper investigate use predictionadaptationcorrection recurrent neural network PACRNNs lowresource speech recognition PACRNN comprised pair neural network correction network us auxiliary information given prediction network help estimate state probability information correction network also used prediction network recurrent loop model outperforms stateoftheart neural network DNNs LSTMs IARPABabel task Moreover transfer learning language similar target language help improve performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna 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592 | 592 | ['Hongyu Guo'] | 1510.09202v1 | We introduce a novel schema for sequence to sequence learning with a Deep
Q-Network (DQN), which decodes the output sequence iteratively. The aim here is
to enable the decoder to first tackle easier portions of the sequences, and
then turn to cope with difficult parts. Specifically, in each iteration, an
encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the
input sequence, automatically create features to represent the internal states
of and formulate a list of potential actions for the DQN. Take rephrasing a
natural sentence as an example. This list can contain ranked potential words.
Next, the DQN learns to make decision on which action (e.g., word) will be
selected from the list to modify the current decoded sequence. The newly
modified output sequence is subsequently used as the input to the DQN for the
next decoding iteration. In each iteration, we also bias the reinforcement
learning's attention to explore sequence portions which are previously
difficult to be decoded. For evaluation, the proposed strategy was trained to
decode ten thousands natural sentences. Our experiments indicate that, when
compared to a left-to-right greedy beam search LSTM decoder, the proposed
method performed competitively well when decoding sentences from the training
set, but significantly outperformed the baseline when decoding unseen
sentences, in terms of BLEU score obtained. | Generating Text with Deep Reinforcement Learning | 2,015 | http://arxiv.org/pdf/1510.09202v1 | Title Generating Text Deep Reinforcement Learning Summary introduce novel schema sequence sequence learning Deep QNetwork DQN decodes output sequence iteratively aim enable decoder first tackle easier portion sequence turn cope difficult part Specifically iteration encoderdecoder Long ShortTerm Memory LSTM network employed input sequence automatically create feature represent internal state formulate list potential action DQN Take rephrasing natural sentence example list contain ranked potential word Next DQN learns make decision action eg word selected list modify current decoded sequence newly modified output sequence subsequently used input DQN next decoding iteration iteration also bias reinforcement learning attention explore sequence portion previously difficult decoded evaluation proposed strategy trained decode ten thousand natural sentence experiment indicate compared lefttoright greedy beam search LSTM decoder proposed method performed competitively well decoding sentence training set significantly outperformed baseline decoding unseen sentence term BLEU score obtained Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.050332311540842056, 0.03927690535783768, 0.002629154361784458, 0.012904432602226734, -0.016218572854995728, -0.004598155617713928, -0.018037695437669754, 0.0040520173497498035, -0.0072245425544679165, -0.015038839541375637, 0.003156442893669009, 0.005189321935176849, -0.0173582062125206, 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593 | 593 | ['Junyoung Chung', 'Jacob Devlin', 'Hany Hassan Awadalla'] | 1511.01042v2 | In this paper, we explore different neural network architectures that can
predict if a speaker of a given utterance is asking a question or making a
statement. We com- pare the outcomes of regularization methods that are
popularly used to train deep neural networks and study how different context
functions can affect the classification performance. We also compare the
efficacy of gated activation functions that are favorably used in recurrent
neural networks and study how to combine multimodal inputs. We evaluate our
models on two multimodal datasets: MSR-Skype and CALLHOME. | Detecting Interrogative Utterances with Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1511.01042v2 | Title Detecting Interrogative Utterances Recurrent Neural Networks Summary paper explore different neural network architecture predict speaker given utterance asking question making statement com pare outcome regularization method popularly used train deep neural network study different context function affect classification performance also compare efficacy gated activation function favorably used recurrent neural network study combine multimodal input evaluate model two multimodal datasets MSRSkype CALLHOME Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07140016555786133, 0.028240378946065903, -0.0030725793913006783, 0.05219525471329689, -0.05397335812449455, -0.021107256412506104, 0.016506560146808624, 0.009133312851190567, 0.00037390904617495835, -0.0003898455179296434, -0.0007419760804623365, -0.021651597693562508, 0.00403172941878438, 0.03598712384700775, 0.008921576663851738, -0.029560929164290428, 0.007916807197034359, 0.03043283149600029, 0.02669430337846279, 0.006550752557814121, 0.05863200128078461, 0.012541805393993855, -0.026771724224090576, 0.03937592729926109, -0.038541220128536224, 0.002687544794753194, -0.011694367043673992, 0.014783695340156555, -0.032499730587005615, -0.03315217047929764, 0.06690960377454758, 0.027952605858445168, -0.007500558625906706, 0.03601102903485298, 2.0404199858603533e-06, -0.023302551358938217, 0.0025301636196672916, -0.03605656698346138, 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594 | 594 | ['Navdeep Jaitly', 'David Sussillo', 'Quoc V. Le', 'Oriol Vinyals', 'Ilya Sutskever', 'Samy Bengio'] | 1511.04868v4 | Sequence-to-sequence models have achieved impressive results on various
tasks. However, they are unsuitable for tasks that require incremental
predictions to be made as more data arrives or tasks that have long input
sequences and output sequences. This is because they generate an output
sequence conditioned on an entire input sequence. In this paper, we present a
Neural Transducer that can make incremental predictions as more input arrives,
without redoing the entire computation. Unlike sequence-to-sequence models, the
Neural Transducer computes the next-step distribution conditioned on the
partially observed input sequence and the partially generated sequence. At each
time step, the transducer can decide to emit zero to many output symbols. The
data can be processed using an encoder and presented as input to the
transducer. The discrete decision to emit a symbol at every time step makes it
difficult to learn with conventional backpropagation. It is however possible to
train the transducer by using a dynamic programming algorithm to generate
target discrete decisions. Our experiments show that the Neural Transducer
works well in settings where it is required to produce output predictions as
data come in. We also find that the Neural Transducer performs well for long
sequences even when attention mechanisms are not used. | A Neural Transducer | 2,015 | http://arxiv.org/pdf/1511.04868v4 | Title Neural Transducer Summary Sequencetosequence model achieved impressive result various task However unsuitable task require incremental prediction made data arrives task long input sequence output sequence generate output sequence conditioned entire input sequence paper present Neural Transducer make incremental prediction input arrives without redoing entire computation Unlike sequencetosequence model Neural Transducer computes nextstep distribution conditioned partially observed input sequence partially generated sequence time step transducer decide emit zero many output symbol data processed using encoder presented input transducer discrete decision emit symbol every time step make difficult learn conventional backpropagation however possible train transducer using dynamic programming algorithm generate target discrete decision experiment show Neural Transducer work well setting required produce output prediction data come also find Neural Transducer performs well long sequence even attention mechanism used Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015562239103019238, 0.054453905671834946, -0.0022053758148103952, -0.013406692072749138, -0.014066972769796848, -0.031723491847515106, 0.003930010832846165, 0.01667247898876667, -0.050774432718753815, -0.0017024906119331717, 0.039740875363349915, 0.01903730072081089, 0.021850761026144028, 0.0713837593793869, 0.03639530763030052, -0.03808370232582092, 0.02743903174996376, 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595 | 595 | ['Ethan Caballero'] | 1511.06420v2 | Question Answering (QA) is fundamental to natural language processing in that
most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly
supervised memory network models that have been proposed so far struggle at
answering questions that involve relations among multiple entities (such as
facebook's bAbi qa5-three-arg-relations in (Weston et al., 2015)). To address
this problem of learning multi-argument multi-hop semantic relations for the
purpose of QA, we propose a method that combines the jointly learned long-term
read-write memory and attentive inference components of end-to-end memory
networks (MemN2N) (Sukhbaatar et al., 2015) with distributed sentence vector
representations encoded by a Skip-Thought model (Kiros et al., 2015). This
choice to append Skip-Thought Vectors to the existing MemN2N framework is
motivated by the fact that Skip-Thought Vectors have been shown to accurately
model multi-argument semantic relations (Kiros et al., 2015). | Skip-Thought Memory Networks | 2,015 | http://arxiv.org/pdf/1511.06420v2 | Title SkipThought Memory Networks Summary Question Answering QA fundamental natural language processing nlp problem phrased QA Kumar et al 2015 Current weakly supervised memory network model proposed far struggle answering question involve relation among multiple entity facebooks bAbi qa5threeargrelations Weston et al 2015 address problem learning multiargument multihop semantic relation purpose QA propose method combine jointly learned longterm readwrite memory attentive inference component endtoend memory network MemN2N Sukhbaatar et al 2015 distributed sentence vector representation encoded SkipThought model Kiros et al 2015 choice append SkipThought Vectors existing MemN2N framework motivated fact SkipThought Vectors shown accurately model multiargument semantic relation Kiros et al 2015 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04113145172595978, 0.017919592559337616, -0.005954056512564421, 0.06399160623550415, -0.021321507170796394, 0.032747529447078705, 0.019004343077540398, -0.027221692726016045, 0.02152428589761257, -0.05416605994105339, -0.015545212663710117, -0.015252524055540562, -0.0282201636582613, 0.023438099771738052, 0.03672714903950691, -0.02666618488729, 0.05208047851920128, 0.021631702780723572, -0.005788791924715042, 0.02837010845541954, 0.020644836127758026, 0.031196705996990204, 0.0017173829255625606, 0.017643773928284645, 0.009765246883034706, -0.04460005834698677, 0.006306048016995192, 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596 | 596 | ['Jason P. C. Chiu', 'Eric Nichols'] | 1511.08308v5 | Named entity recognition is a challenging task that has traditionally
required large amounts of knowledge in the form of feature engineering and
lexicons to achieve high performance. In this paper, we present a novel neural
network architecture that automatically detects word- and character-level
features using a hybrid bidirectional LSTM and CNN architecture, eliminating
the need for most feature engineering. We also propose a novel method of
encoding partial lexicon matches in neural networks and compare it to existing
approaches. Extensive evaluation shows that, given only tokenized text and
publicly available word embeddings, our system is competitive on the CoNLL-2003
dataset and surpasses the previously reported state of the art performance on
the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed
from publicly-available sources, we establish new state of the art performance
with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing
systems that employ heavy feature engineering, proprietary lexicons, and rich
entity linking information. | Named Entity Recognition with Bidirectional LSTM-CNNs | 2,015 | http://arxiv.org/pdf/1511.08308v5 | Title Named Entity Recognition Bidirectional LSTMCNNs Summary Named entity recognition challenging task traditionally required large amount knowledge form feature engineering lexicon achieve high performance paper present novel neural network architecture automatically detects word characterlevel feature using hybrid bidirectional LSTM CNN architecture eliminating need feature engineering also propose novel method encoding partial lexicon match neural network compare existing approach Extensive evaluation show given tokenized text publicly available word embeddings system competitive CoNLL2003 dataset surpasses previously reported state art performance OntoNotes 50 dataset 213 F1 point using two lexicon constructed publiclyavailable source establish new state art performance F1 score 9162 CoNLL2003 8628 OntoNotes surpassing system employ heavy feature engineering proprietary lexicon rich entity linking information Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.024691695347428322, 0.0367535836994648, 0.033279772847890854, 0.09188473224639893, -0.01054808497428894, 0.02476241998374462, 0.010465210303664207, 0.051607243716716766, 0.016980402171611786, -0.05492996796965599, -0.009933359920978546, -0.03147837892174721, -0.02064288593828678, 0.055758364498615265, 0.0329645574092865, -0.004755240865051746, 0.020480819046497345, 0.019315583631396294, 0.0024142644833773375, 0.014024721458554268, 0.002212621970102191, 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597 | 597 | ['Konstantin Lopyrev'] | 1512.01712v1 | We describe an application of an encoder-decoder recurrent neural network
with LSTM units and attention to generating headlines from the text of news
articles. We find that the model is quite effective at concisely paraphrasing
news articles. Furthermore, we study how the neural network decides which input
words to pay attention to, and specifically we identify the function of the
different neurons in a simplified attention mechanism. Interestingly, our
simplified attention mechanism performs better that the more complex attention
mechanism on a held out set of articles. | Generating News Headlines with Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1512.01712v1 | Title Generating News Headlines Recurrent Neural Networks Summary describe application encoderdecoder recurrent neural network LSTM unit attention generating headline text news article find model quite effective concisely paraphrasing news article Furthermore study neural network decides input word pay attention specifically identify function different neuron simplified attention mechanism Interestingly simplified attention mechanism performs better complex attention mechanism held set article Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang 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598 | 598 | ['Pranjal Singh', 'Amitabha Mukerjee'] | 1512.03549v1 | Despite the success of distributional semantics, composing phrases from word
vectors remains an important challenge. Several methods have been tried for
benchmark tasks such as sentiment classification, including word vector
averaging, matrix-vector approaches based on parsing, and on-the-fly learning
of paragraph vectors. Most models usually omit stop words from the composition.
Instead of such an yes-no decision, we consider several graded schemes where
words are weighted according to their discriminatory relevance with respect to
its use in the document (e.g., idf). Some of these methods (particularly
tf-idf) are seen to result in a significant improvement in performance over
prior state of the art. Further, combining such approaches into an ensemble
based on alternate classifiers such as the RNN model, results in an 1.6%
performance improvement on the standard IMDB movie review dataset, and a 7.01%
improvement on Amazon product reviews. Since these are language free models and
can be obtained in an unsupervised manner, they are of interest also for
under-resourced languages such as Hindi as well and many more languages. We
demonstrate the language free aspects by showing a gain of 12% for two review
datasets over earlier results, and also release a new larger dataset for future
testing (Singh,2015). | Words are not Equal: Graded Weighting Model for building Composite
Document Vectors | 2,015 | http://arxiv.org/pdf/1512.03549v1 | Title Words Equal Graded Weighting Model building Composite Document Vectors Summary Despite success distributional semantics composing phrase word vector remains important challenge Several method tried benchmark task sentiment classification including word vector averaging matrixvector approach based parsing onthefly learning paragraph vector model usually omit stop word composition Instead yesno decision consider several graded scheme word weighted according discriminatory relevance respect use document eg idf method particularly tfidf seen result significant improvement performance prior state art combining approach ensemble based alternate classifier RNN model result 16 performance improvement standard IMDB movie review dataset 701 improvement Amazon product review Since language free model obtained unsupervised manner interest also underresourced language Hindi well many language demonstrate language free aspect showing gain 12 two review datasets earlier result also release new larger dataset future testing Singh2015 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.08187291771173477, 0.06270714104175568, 0.017307894304394722, 0.03457587584853172, -0.05003199353814125, 0.01512089278548956, 0.007526844274252653, 0.031498175114393234, -0.04426541179418564, -0.07682067900896072, -0.004928107373416424, -0.02226550504565239, 0.03401249274611473, 0.014045490883290768, -0.0230861809104681, -0.038819801062345505, 0.003318122122436762, 0.05424780398607254, 0.036647483706474304, 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599 | 599 | ['Liang Lu', 'Steve Renals'] | 1512.04280v4 | For speech recognition, deep neural networks (DNNs) have significantly
improved the recognition accuracy in most of benchmark datasets and application
domains. However, compared to the conventional Gaussian mixture models,
DNN-based acoustic models usually have much larger number of model parameters,
making it challenging for their applications in resource constrained platforms,
e.g., mobile devices. In this paper, we study the application of the recently
proposed highway network to train small-footprint DNNs, which are {\it thinner}
and {\it deeper}, and have significantly smaller number of model parameters
compared to conventional DNNs. We investigated this approach on the AMI meeting
speech transcription corpus which has around 70 hours of audio data. The
highway neural networks constantly outperformed their plain DNN counterparts,
and the number of model parameters can be reduced significantly without
sacrificing the recognition accuracy. | Small-footprint Deep Neural Networks with Highway Connections for Speech
Recognition | 2,015 | http://arxiv.org/pdf/1512.04280v4 | Title Smallfootprint Deep Neural Networks Highway Connections Speech Recognition Summary speech recognition deep neural network DNNs significantly improved recognition accuracy benchmark datasets application domain However compared conventional Gaussian mixture model DNNbased acoustic model usually much larger number model parameter making challenging application resource constrained platform eg mobile device paper study application recently proposed highway network train smallfootprint DNNs thinner deeper significantly smaller number model parameter compared conventional DNNs investigated approach AMI meeting speech transcription corpus around 70 hour audio data highway neural network constantly outperformed plain DNN counterpart number model parameter reduced significantly without sacrificing recognition accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02974539063870907, 0.05479646474123001, -0.020276816561818123, 0.05071445554494858, -0.0004927068948745728, -0.015778936445713043, 0.05087105557322502, -0.005609317682683468, -0.06028558313846588, 0.004958346951752901, -0.001501046703197062, -0.013900922611355782, 0.052471064031124115, 0.02007078193128109, 0.01700573042035103, -0.05429249629378319, -0.018748106434941292, -0.01609550230205059, 0.005278367083519697, -0.02203374169766903, 0.014599532820284367, -0.006125510670244694, -0.033125460147857666, 0.0482231080532074, -0.0660259947180748, 0.059319812804460526, 0.008817225694656372, 0.020460834726691246, 0.006833844818174839, 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