pwc-archive/papers-with-abstracts huggingface dataset:
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
Authors: Tara N. Sainath, Rohit Prabhavalkar, Shankar Kumar, Seungji Lee, Anjuli Kannan, David Rybach, Vlad Schogol, Patrick Nguyen, Bo Li, Yonghui Wu, Zhifeng Chen, Chung-Cheng Chiu
Date: 2017-12-05 00:00:00
Tasks: Language Modeling, Language Modelling

For decades, context-dependent phonemes have been the dominant sub-word unit
for conventional acoustic modeling systems. This status quo has begun to be
challenged recently by end-to-end models which seek to combine acoustic,
pronunciation, and language model components into a single neural network. Such
systems, which typically predict graphemes or words, simplify the recognition
process since they remove the need for a separate expert-curated pronunciation
lexicon to map from phoneme-based units to words. However, there has been
little previous work comparing phoneme-based versus grapheme-based sub-word
units in the end-to-end modeling framework, to determine whether the gains from
such approaches are primarily due to the new probabilistic model, or from the
joint learning of the various components with grapheme-based units.
  In this work, we conduct detailed experiments which are aimed at quantifying
the value of phoneme-based pronunciation lexica in the context of end-to-end
models. We examine phoneme-based end-to-end models, which are contrasted
against grapheme-based ones on a large vocabulary English Voice-search task,
where we find that graphemes do indeed outperform phonemes. We also compare
grapheme and phoneme-based approaches on a multi-dialect English task, which
once again confirm the superiority of graphemes, greatly simplifying the system
for recognizing multiple dialects.
Neural Machine Translation by Generating Multiple Linguistic Factors
Authors: Mercedes García-Martínez, Loïc Barrault, Fethi Bougares
Date: 2017-12-05 00:00:00
Tasks: Machine Translation, NMT, Translation

Factored neural machine translation (FNMT) is founded on the idea of using
the morphological and grammatical decomposition of the words (factors) at the
output side of the neural network. This architecture addresses two well-known
problems occurring in MT, namely the size of target language vocabulary and the
number of unknown tokens produced in the translation. FNMT system is designed
to manage larger vocabulary and reduce the training time (for systems with
equivalent target language vocabulary size). Moreover, we can produce
grammatically correct words that are not part of the vocabulary. FNMT model is
evaluated on IWSLT'15 English to French task and compared to the baseline
word-based and BPE-based NMT systems. Promising qualitative and quantitative
results (in terms of BLEU and METEOR) are reported.
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Authors: Rohit Prabhavalkar, Tara N. Sainath, Yonghui Wu, Patrick Nguyen, Zhifeng Chen, Chung-Cheng Chiu, Anjuli Kannan
Date: 2017-12-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Sequence-to-sequence models, such as attention-based models in automatic
speech recognition (ASR), are typically trained to optimize the cross-entropy
criterion which corresponds to improving the log-likelihood of the data.
However, system performance is usually measured in terms of word error rate
(WER), not log-likelihood. Traditional ASR systems benefit from discriminative
sequence training which optimizes criteria such as the state-level minimum
Bayes risk (sMBR) which are more closely related to WER. In the present work,
we explore techniques to train attention-based models to directly minimize
expected word error rate. We consider two loss functions which approximate the
expected number of word errors: either by sampling from the model, or by using
N-best lists of decoded hypotheses, which we find to be more effective than the
sampling-based method. In experimental evaluations, we find that the proposed
training procedure improves performance by up to 8.2% relative to the baseline
system. This allows us to train grapheme-based, uni-directional attention-based
models which match the performance of a traditional, state-of-the-art,
discriminative sequence-trained system on a mobile voice-search task.
WNtags: A Web-Based Tool For Image Labeling And Retrieval With Lexical Ontologies
Authors: Marko Horvat, Anton Grbin, Gordan Gledec
Date: 2013-02-09 00:00:00
Tasks: Retrieval

Ever growing number of image documents available on the Internet continuously
motivates research in better annotation models and more efficient retrieval
methods. Formal knowledge representation of objects and events in pictures,
their interaction as well as context complexity becomes no longer an option for
a quality image repository, but a necessity. We present an ontology-based
online image annotation tool WNtags and demonstrate its usefulness in several
typical multimedia retrieval tasks using International Affective Picture System
emotionally annotated image database. WNtags is built around WordNet lexical
ontology but considers Suggested Upper Merged Ontology as the preferred
labeling formalism. WNtags uses sets of weighted WordNet synsets as high-level
image semantic descriptors and query matching is performed with word stemming
and node distance metrics. We also elaborate our near future plans to expand
image content description with induced affect as in stimuli for research of
human emotion and attention.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Authors: David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent SIfre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis
Date: 2017-12-05 00:00:00
Tasks: Game of Chess, Game of Go, Game of Shogi, General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The game of chess is the most widely-studied domain in the history of
artificial intelligence. The strongest programs are based on a combination of
sophisticated search techniques, domain-specific adaptations, and handcrafted
evaluation functions that have been refined by human experts over several
decades. In contrast, the AlphaGo Zero program recently achieved superhuman
performance in the game of Go, by tabula rasa reinforcement learning from games
of self-play. In this paper, we generalise this approach into a single
AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain
knowledge except the game rules, AlphaZero achieved within 24 hours a
superhuman level of play in the games of chess and shogi (Japanese chess) as
well as Go, and convincingly defeated a world-champion program in each case.
Neural Cross-Lingual Entity Linking
Authors: Avirup Sil, Gourab Kundu, Radu Florian, Wael Hamza
Date: 2017-12-05 00:00:00
Tasks: Cross-Lingual Entity Linking, Entity Disambiguation, Entity Linking, Tensor Networks, Zero-Shot Learning

A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets.
Improving the Performance of Online Neural Transducer Models
Authors: Tara N. Sainath, Chung-Cheng Chiu, Rohit Prabhavalkar, Anjuli Kannan, Yonghui Wu, Patrick Nguyen, Zhifeng Chen
Date: 2017-12-05 00:00:00

Having a sequence-to-sequence model which can operate in an online fashion is
important for streaming applications such as Voice Search. Neural transducer is
a streaming sequence-to-sequence model, but has shown a significant degradation
in performance compared to non-streaming models such as Listen, Attend and
Spell (LAS). In this paper, we present various improvements to NT.
Specifically, we look at increasing the window over which NT computes
attention, mainly by looking backwards in time so the model still remains
online. In addition, we explore initializing a NT model from a LAS-trained
model so that it is guided with a better alignment. Finally, we explore
including stronger language models such as using wordpiece models, and applying
an external LM during the beam search. On a Voice Search task, we find with
these improvements we can get NT to match the performance of LAS.
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Authors: Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Semantic Similarity, Semantic Textual Similarity

Semi-supervised learning methods using Generative Adversarial Networks (GANs)
have shown promising empirical success recently. Most of these methods use a
shared discriminator/classifier which discriminates real examples from fake
while also predicting the class label. Motivated by the ability of the GANs
generator to capture the data manifold well, we propose to estimate the tangent
space to the data manifold using GANs and employ it to inject invariances into
the classifier. In the process, we propose enhancements over existing methods
for learning the inverse mapping (i.e., the encoder) which greatly improves in
terms of semantic similarity of the reconstructed sample with the input sample.
We observe considerable empirical gains in semi-supervised learning over
baselines, particularly in the cases when the number of labeled examples is
low. We also provide insights into how fake examples influence the
semi-supervised learning procedure.
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Authors: Bharat Singh, Hengduo Li, Abhishek Sharma, Larry S. Davis
Date: 2017-12-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, Object, Position

We present R-FCN-3000, a large-scale real-time object detector in which
objectness detection and classification are decoupled. To obtain the detection
score for an RoI, we multiply the objectness score with the fine-grained
classification score. Our approach is a modification of the R-FCN architecture
in which position-sensitive filters are shared across different object classes
for performing localization. For fine-grained classification, these
position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9%
on the ImageNet detection dataset and outperforms YOLO-9000 by 18% while
processing 30 images per second. We also show that the objectness learned by
R-FCN-3000 generalizes to novel classes and the performance increases with the
number of training object classes - supporting the hypothesis that it is
possible to learn a universal objectness detector. Code will be made available.
One for All: Towards Language Independent Named Entity Linking
Authors: Avirup Sil, Radu Florian
Date: 2017-12-05 00:00:00
Proceeding: ACL 2016 8
Tasks: All, Entity Linking, Reranking

Entity linking (EL) is the task of disambiguating mentions in text by
associating them with entries in a predefined database of mentions (persons,
organizations, etc). Most previous EL research has focused mainly on one
language, English, with less attention being paid to other languages, such as
Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent
Entity Linking system, which provides an EL framework which, once trained on
one language, works remarkably well on a number of different languages without
change. LIEL makes a joint global prediction over the entire document,
employing a discriminative reranking framework with many domain and
language-independent feature functions. Experiments on numerous benchmark
datasets, show that the proposed system, once trained on one language, English,
outperforms several state-of-the-art systems in English (by 4 points) and the
trained model also works very well on Spanish (14 points better than a
competitor system), demonstrating the viability of the approach.
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Authors: Svetlana Kiritchenko, Saif M. Mohammad
Date: 2017-12-05 00:00:00
Proceeding: WS 2016 6

Negators, modals, and degree adverbs can significantly affect the sentiment
of the words they modify. Often, their impact is modeled with simple
heuristics; although, recent work has shown that such heuristics do not capture
the true sentiment of multi-word phrases. We created a dataset of phrases that
include various negators, modals, and degree adverbs, as well as their
combinations. Both the phrases and their constituent content words were
annotated with real-valued scores of sentiment association. Using phrasal terms
in the created dataset, we analyze the impact of individual modifiers and the
average effect of the groups of modifiers on overall sentiment. We find that
the effect of modifiers varies substantially among the members of the same
group. Furthermore, each individual modifier can affect sentiment words in
different ways. Therefore, solutions based on statistical learning seem more
promising than fixed hand-crafted rules on the task of automatic sentiment
prediction.
Rethinking Atrous Convolution for Semantic Image Segmentation
Authors: Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam
Date: 2017-06-17 00:00:00
Tasks: 2D Semantic Segmentation, Dichotomous Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scales, we design modules which employ atrous convolution in cascade
or in parallel to capture multi-scale context by adopting multiple atrous
rates. Furthermore, we propose to augment our previously proposed Atrous
Spatial Pyramid Pooling module, which probes convolutional features at multiple
scales, with image-level features encoding global context and further boost
performance. We also elaborate on implementation details and share our
experience on training our system. The proposed `DeepLabv3' system
significantly improves over our previous DeepLab versions without DenseCRF
post-processing and attains comparable performance with other state-of-art
models on the PASCAL VOC 2012 semantic image segmentation benchmark.
Slope Stability Analysis with Geometric Semantic Genetic Programming
Authors: Juncai Xu, Zhenzhong Shen, Qingwen Ren, Xin Xie, Zhengyu Yang
Date: 2017-08-30 00:00:00
Tasks: General Classification, regression

Genetic programming has been widely used in the engineering field. Compared
with the conventional genetic programming and artificial neural network,
geometric semantic genetic programming (GSGP) is superior in astringency and
computing efficiency. In this paper, GSGP is adopted for the classification and
regression analysis of a sample dataset. Furthermore, a model for slope
stability analysis is established on the basis of geometric semantics.
According to the results of the study based on GSGP, the method can analyze
slope stability objectively and is highly precise in predicting slope stability
and safety factors. Hence, the predicted results can be used as a reference for
slope safety design.
Automated Website Fingerprinting through Deep Learning
Authors: Vera Rimmer, Davy Preuveneers, Marc Juarez, Tom Van Goethem, Wouter Joosen
Date: 2017-08-21 00:00:00
Tasks: Deep Learning, Feature Engineering

Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.
Explicablility as Minimizing Distance from Expected Behavior
Authors: Anagha Kulkarni, Yantian Zha, Tathagata Chakraborti, Satya Gautam Vadlamudi, Yu Zhang, Subbarao Kambhampati
Date: 2016-11-16 00:00:00

In order to have effective human-AI collaboration, it is necessary to address
how the AI agent's behavior is being perceived by the humans-in-the-loop. When
the agent's task plans are generated without such considerations, they may
often demonstrate inexplicable behavior from the human's point of view. This
problem may arise due to the human's partial or inaccurate understanding of the
agent's planning model. This may have serious implications from increased
cognitive load to more serious concerns of safety around a physical agent. In
this paper, we address this issue by modeling plan explicability as a function
of the distance between a plan that agent makes and the plan that human expects
it to make. We learn a regression model for mapping the plan distances to
explicability scores of plans and develop an anytime search algorithm that can
use this model as a heuristic to come up with progressively explicable plans.
We evaluate the effectiveness of our approach in a simulated autonomous car
domain and a physical robot domain.
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
Authors: Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
Date: 2017-12-05 00:00:00
Tasks: Hyperspectral Unmixing, Superpixels

Sparse hyperspectral unmixing from large spectral libraries has been
considered to circumvent limitations of endmember extraction algorithms in many
applications. This strategy often leads to ill-posed inverse problems, which
can benefit from spatial regularization strategies. While existing spatial
regularization methods improve the problem conditioning and promote piecewise
smooth solutions, they lead to large nonsmooth optimization problems. Thus,
efficiently introducing spatial context in the unmixing problem remains a
challenge, and a necessity for many real world applications. In this paper, a
novel multiscale spatial regularization approach for sparse unmixing is
proposed. The method uses a signal-adaptive spatial multiscale decomposition
based on superpixels to decompose the unmixing problem into two simpler
problems, one in the approximation domain and another in the original domain.
Simulation results using both synthetic and real data indicate that the
proposed method can outperform state-of-the-art Total Variation-based
algorithms with a computation time comparable to that of their unregularized
counterparts.
Predicting Discharge Medications at Admission Time Based on Deep Learning
Authors: Yuan Yang, Pengtao Xie, Xin Gao, Carol Cheng, Christy Li, Hongbao Zhang, Eric Xing
Date: 2017-11-04 00:00:00
Tasks: Deep Learning

Predicting discharge medications right after a patient being admitted is an
important clinical decision, which provides physicians with guidance on what
type of medication regimen to plan for and what possible changes on initial
medication may occur during an inpatient stay. It also facilitates medication
reconciliation process with easy detection of medication discrepancy at
discharge time to improve patient safety. However, since the information
available upon admission is limited and patients' condition may evolve during
an inpatient stay, these predictions could be a difficult decision for
physicians to make. In this work, we investigate how to leverage deep learning
technologies to assist physicians in predicting discharge medications based on
information documented in the admission note. We build a convolutional neural
network which takes an admission note as input and predicts the medications
placed on the patient at discharge time. Our method is able to distill semantic
patterns from unstructured and noisy texts, and is capable of capturing the
pharmacological correlations among medications. We evaluate our method on 25K
patient visits and compare with 4 strong baselines. Our methods demonstrate a
20% increase in macro-averaged F1 score than the best baseline.
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Authors: Svetlana Kiritchenko, Saif M. Mohammad
Date: 2017-12-05 00:00:00
Proceeding: ACL 2017 7

Rating scales are a widely used method for data annotation; however, they
present several challenges, such as difficulty in maintaining inter- and
intra-annotator consistency. Best-worst scaling (BWS) is an alternative method
of annotation that is claimed to produce high-quality annotations while keeping
the required number of annotations similar to that of rating scales. However,
the veracity of this claim has never been systematically established. Here for
the first time, we set up an experiment that directly compares the rating scale
method with BWS. We show that with the same total number of annotations, BWS
produces significantly more reliable results than the rating scale.