pwc-archive/papers-with-abstracts huggingface dataset:
Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Authors: Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime Carbonell
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

For languages with no annotated resources, unsupervised transfer of natural
language processing models such as named-entity recognition (NER) from
resource-rich languages would be an appealing capability. However, differences
in words and word order across languages make it a challenging problem. To
improve mapping of lexical items across languages, we propose a method that
finds translations based on bilingual word embeddings. To improve robustness to
word order differences, we propose to use self-attention, which allows for a
degree of flexibility with respect to word order. We demonstrate that these
methods achieve state-of-the-art or competitive NER performance on commonly
tested languages under a cross-lingual setting, with much lower resource
requirements than past approaches. We also evaluate the challenges of applying
these methods to Uyghur, a low-resource language.
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation
Authors: Jie Chen, Yu Zeng
Date: 2018-08-29 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Decision Making, Facies Classification, Feature Engineering, General Classification

With recent progress in algorithms and the availability of massive amounts of
computation power, application of machine learning techniques is becoming a hot
topic in the oil and gas industry. One of the most promising aspects to apply
machine learning to the upstream field is the rock facies classification in
reservoir characterization, which is crucial in determining the net pay
thickness of reservoirs, thus a definitive factor in drilling decision making
process. For complex machine learning tasks like facies classification, feature
engineering is often critical. This paper shows the inclusion of
physics-motivated feature interaction in feature augmentation can further
improve the capability of machine learning in rock facies classification. We
demonstrate this approach with the SEG 2016 machine learning contest dataset
and the top winning algorithms. The improvement is roboust and can be $\sim5\%$
better than current existing best F-1 score, where F-1 is an evaluation metric
used to quantify average prediction accuracy.
Modelling Langford's Problem: A Viewpoint for Search
Authors: Özgür Akgün, Ian Miguel
Date: 2018-08-29 00:00:00
Tasks: All

The performance of enumerating all solutions to an instance of Langford's
Problem is sensitive to the model and the search strategy. In this paper we
compare the performance of a large variety of models, all derived from two base
viewpoints. We empirically show that a channelled model with a static branching
order on one of the viewpoints offers the best performance out of all the
options we consider. Surprisingly, one of the base models proves very effective
for propagation, while the other provides an effective means of stating a
static search order.
Searching Toward Pareto-Optimal Device-Aware Neural Architectures
Authors: An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Date: 2018-08-29 00:00:00
Tasks: image-classification, Image Classification

Recent breakthroughs in Neural Architectural Search (NAS) have achieved
state-of-the-art performance in many tasks such as image classification and
language understanding. However, most existing works only optimize for model
accuracy and largely ignore other important factors imposed by the underlying
hardware and devices, such as latency and energy, when making inference. In
this paper, we first introduce the problem of NAS and provide a survey on
recent works. Then we deep dive into two recent advancements on extending NAS
into multiple-objective frameworks: MONAS and DPP-Net. Both MONAS and DPP-Net
are capable of optimizing accuracy and other objectives imposed by devices,
searching for neural architectures that can be best deployed on a wide spectrum
of devices: from embedded systems and mobile devices to workstations.
Experimental results are poised to show that architectures found by MONAS and
DPP-Net achieves Pareto optimality w.r.t the given objectives for various
devices.
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
Authors: Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Estefania Talavera, Syeda Furruka Banu, Petia Radeva, Domenec Puig
Date: 2018-08-29 00:00:00
Tasks: General Classification

First-person (wearable) camera continually captures unscripted interactions
of the camera user with objects, people, and scenes reflecting his personal and
relational tendencies. One of the preferences of people is their interaction
with food events. The regulation of food intake and its duration has a great
importance to protect against diseases. Consequently, this work aims to develop
a smart model that is able to determine the recurrences of a person on food
places during a day. This model is based on a deep end-to-end model for
automatic food places recognition by analyzing egocentric photo-streams. In
this paper, we apply multi-scale Atrous convolution networks to extract the key
features related to food places of the input images. The proposed model is
evaluated on an in-house private dataset called "EgoFoodPlaces". Experimental
results shows promising results of food places classification recognition in
egocentric photo-streams.
Approximate Exploration through State Abstraction
Authors: Adrien Ali Taïga, Aaron Courville, Marc G. Bellemare
Date: 2018-08-29 00:00:00
Tasks: Reinforcement Learning

Although exploration in reinforcement learning is well understood from a
theoretical point of view, provably correct methods remain impractical. In this
paper we study the interplay between exploration and approximation, what we
call approximate exploration. Our main goal is to further our theoretical
understanding of pseudo-count based exploration bonuses (Bellemare et al.,
2016), a practical exploration scheme based on density modelling. As a warm-up,
we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and
Littman, 2008), when explicitly combined with state aggregation. This allows us
to confirm that, as might be expected, approximation allows the agent to trade
off between learning speed and quality of the learned policy. Next, we show how
a given density model can be related to an abstraction and that the
corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined
with this abstraction, but may lead to either under- or over-exploration. Then,
we show that a given density model also defines an implicit abstraction, and
find a surprising mismatch between pseudo-counts derived either implicitly or
explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.
Interact as You Intend: Intention-Driven Human-Object Interaction Detection
Authors: Bingjie Xu, Junnan Li, Yongkang Wong, Mohan S. Kankanhalli, Qi Zhao
Date: 2018-08-29 00:00:00
Tasks: Human-Object Interaction Detection

The recent advances in instance-level detection tasks lay strong foundation for genuine comprehension of the visual scenes. However, the ability to fully comprehend a social scene is still in its preliminary stage. In this work, we focus on detecting human-object interactions (HOIs) in social scene images, which is demanding in terms of research and increasingly useful for practical applications. To undertake social tasks interacting with objects, humans direct their attention and move their body based on their intention. Based on this observation, we provide a unique computational perspective to explore human intention in HOI detection. Specifically, the proposed human intention-driven HOI detection (iHOI) framework models human pose with the relative distances from body joints to the object instances. It also utilizes human gaze to guide the attended contextual regions in a weakly-supervised setting. In addition, we propose a hard negative sampling strategy to address the problem of mis-grouping. We perform extensive experiments on two benchmark datasets, namely V-COCO and HICO-DET. The efficacy of each proposed component has also been validated.
Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results
Authors: Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, Bin Yang
Date: 2018-08-29 00:00:00
Tasks: Correlated Time Series Forecasting, Multi-Task Learning, Time Series, Time Series Analysis, Time Series Forecasting

Cyber-physical systems often consist of entities that interact with each
other over time. Meanwhile, as part of the continued digitization of industrial
processes, various sensor technologies are deployed that enable us to record
time-varying attributes (a.k.a., time series) of such entities, thus producing
correlated time series. To enable accurate forecasting on such correlated time
series, this paper proposes two models that combine convolutional neural
networks (CNNs) and recurrent neural networks (RNNs). The first model employs a
CNN on each individual time series, combines the convoluted features, and then
applies an RNN on top of the convoluted features in the end to enable
forecasting. The second model adds additional auto-encoders into the individual
CNNs, making the second model a multi-task learning model, which provides
accurate and robust forecasting. Experiments on two real-world correlated time
series data set suggest that the proposed two models are effective and
outperform baselines in most settings.
  This report extends the paper "Correlated Time Series Forecasting using
Multi-Task Deep Neural Networks," to appear in ACM CIKM 2018, by providing
additional experimental results.
Notes on Deep Learning for NLP
Authors: Antoine J. -P. Tixier
Date: 2018-08-29 00:00:00
Tasks: Deep Learning

My notes on Deep Learning for NLP.
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification
Authors: Yao Qin, Lorenzo Bruzzone, Biao Li
Date: 2018-08-29 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Superpixels

This paper presents a tensor alignment (TA) based domain adaptation method
for hyperspectral image (HSI) classification. To be specific, HSIs in both
domains are first segmented into superpixels and tensors of both domains are
constructed to include neighboring samples from single superpixel. Then we
consider the subspace invariance between two domains as projection matrices and
original tensors are projected as core tensors with lower dimensions into the
invariant tensor subspace by applying Tucker decomposition. To preserve
geometric information in original tensors, we employ a manifold regularization
term for core tensors into the decomposition progress. The projection matrices
and core tensors are solved in an alternating optimization manner and the
convergence of TA algorithm is analyzed. In addition, a post-processing
strategy is defined via pure samples extraction for each superpixel to further
improve classification performance. Experimental results on four real HSIs
demonstrate that the proposed method can achieve better performance compared
with the state-of-the-art subspace learning methods when a limited amount of
source labeled samples are available.
Rule induction for global explanation of trained models
Authors: Madhumita Sushil, Simon Šuster, Walter Daelemans
Date: 2018-08-29 00:00:00
Proceeding: WS 2018 11
Tasks: Feature Importance, text-classification, Text Classification

Understanding the behavior of a trained network and finding explanations for
its outputs is important for improving the network's performance and
generalization ability, and for ensuring trust in automated systems. Several
approaches have previously been proposed to identify and visualize the most
important features by analyzing a trained network. However, the relations
between different features and classes are lost in most cases. We propose a
technique to induce sets of if-then-else rules that capture these relations to
globally explain the predictions of a network. We first calculate the
importance of the features in the trained network. We then weigh the original
inputs with these feature importance scores, simplify the transformed input
space, and finally fit a rule induction model to explain the model predictions.
We find that the output rule-sets can explain the predictions of a neural
network trained for 4-class text classification from the 20 newsgroups dataset
to a macro-averaged F-score of 0.80. We make the code available at
https://github.com/clips/interpret_with_rules.
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification
Authors: Yao Qin, Lorenzo Bruzzone, Biao Li, Yuanxin Ye
Date: 2018-08-29 00:00:00
Tasks: Domain Adaptation, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

This paper introduces a novel heterogenous domain adaptation (HDA) method for
hyperspectral image classification with a limited amount of labeled samples in
both domains. The method is achieved in the way of cross-domain collaborative
learning (CDCL), which is addressed via cluster canonical correlation analysis
(C-CCA) and random walker (RW) algorithms. To be specific, the proposed CDCL
method is an iterative process of three main stages, i.e. twice of RW-based
pseudolabeling and cross domain learning via C-CCA. Firstly, given the
initially labeled target samples as training set ($\mathbf{TS}$), the RW-based
pseudolabeling is employed to update $\mathbf{TS}$ and extract target clusters
($\mathbf{TCs}$) by fusing the segmentation results obtained by RW and extended
RW (ERW) classifiers. Secondly, cross domain learning via C-CCA is applied
using labeled source samples and $\mathbf{TCs}$. The unlabeled target samples
are then classified with the estimated probability maps using the model trained
in the projected correlation subspace. Thirdly, both $\mathbf{TS}$ and
estimated probability maps are used for updating $\mathbf{TS}$ again via
RW-based pseudolabeling. When the iterative process finishes, the result
obtained by the ERW classifier using the final $\mathbf{TS}$ and estimated
probability maps is regarded as the final classification map. Experimental
results on four real HSIs demonstrate that the proposed method can achieve
better performance compared with the state-of-the-art HDA and ERW methods.
Bringing personalized learning into computer-aided question generation
Authors: Yi-Ting Huang, Meng Chang Chen, Yeali S. Sun
Date: 2018-08-29 00:00:00
Tasks: Question Generation, Question-Generation

This paper proposes a novel and statistical method of ability estimation
based on acquisition distribution for a personalized computer aided question
generation. This method captures the learning outcomes over time and provides a
flexible measurement based on the acquisition distributions instead of
precalibration. Compared to the previous studies, the proposed method is
robust, especially when an ability of a student is unknown. The results from
the empirical data show that the estimated abilities match the actual abilities
of learners, and the pretest and post-test of the experimental group show
significant improvement. These results suggest that this method can serves as
the ability estimation for a personalized computer-aided testing environment.
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging
Authors: Barbara Plank, Željko Agić
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Part-Of-Speech Tagging, TAG

We introduce DsDs: a cross-lingual neural part-of-speech tagger that learns
from disparate sources of distant supervision, and realistically scales to
hundreds of low-resource languages. The model exploits annotation projection,
instance selection, tag dictionaries, morphological lexicons, and distributed
representations, all in a uniform framework. The approach is simple, yet
surprisingly effective, resulting in a new state of the art without access to
any gold annotated data.
Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes
Authors: Yi-Ting Huang, Meng Chang Chen, Yeali S. Sun
Date: 2018-08-29 00:00:00
Tasks: Multiple-choice, Question Generation, Question-Generation, Reading Comprehension

In the last several years, the field of computer assisted language learning
has increasingly focused on computer aided question generation. However, this
approach often provides test takers with an exhaustive amount of questions that
are not designed for any specific testing purpose. In this work, we present a
personalized computer aided question generation that generates multiple choice
questions at various difficulty levels and types, including vocabulary, grammar
and reading comprehension. In order to improve the weaknesses of test takers,
it selects questions depending on an estimated proficiency level and unclear
concepts behind incorrect responses. This results show that the students with
the personalized automatic quiz generation corrected their mistakes more
frequently than ones only with computer aided question generation. Moreover,
students demonstrated the most progress between the pretest and post test and
correctly answered more difficult questions. Finally, we investigated the
personalizing strategy and found that a student could make a significant
progress if the proposed system offered the vocabulary questions at the same
level of his or her proficiency level, and if the grammar and reading
comprehension questions were at a level lower than his or her proficiency
level.
Identifying the sentiment styles of YouTube's vloggers
Authors: Bennett Kleinberg, Maximilian Mozes, Isabelle van der Vegt
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Sentiment Analysis

Vlogs provide a rich public source of data in a novel setting. This paper
examined the continuous sentiment styles employed in 27,333 vlogs using a
dynamic intra-textual approach to sentiment analysis. Using unsupervised
clustering, we identified seven distinct continuous sentiment trajectories
characterized by fluctuations of sentiment throughout a vlog's narrative time.
We provide a taxonomy of these seven continuous sentiment styles and found that
vlogs whose sentiment builds up towards a positive ending are the most
prevalent in our sample. Gender was associated with preferences for different
continuous sentiment trajectories. This paper discusses the findings with
respect to previous work and concludes with an outlook towards possible uses of
the corpus, method and findings of this paper for related areas of research.
Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers
Authors: Yi-Ting Huang, Meng Chang Chen, Yeali S. Sun
Date: 2018-08-29 00:00:00
Tasks: Information Retrieval, Novel Concepts, Retrieval

In recent years, the number of people studying English as a second language
(ESL) has surpassed the number of native speakers. Recent work have
demonstrated the success of providing personalized content based on reading
difficulty, such as information retrieval and summarization. However, almost
all prior studies of reading difficulty are designed for native speakers,
rather than non-native readers. In this study, we investigate various features
for ESL readers, by conducting a linear regression to estimate the reading
level of English language sources. This estimation is based not only on the
complexity of lexical and syntactic features, but also several novel concepts,
including the age of word and grammar acquisition from several sources, word
sense from WordNet, and the implicit relation between sentences. By employing
Bayesian Information Criterion (BIC) to select the optimal model, we find that
the combination of the number of words, the age of word acquisition and the
height of the parsing tree generate better results than alternative competing
models. Thus, our results show that proposed second language reading difficulty
estimation outperforms other first language reading difficulty estimations.
What can we learn from Semantic Tagging?
Authors: Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, Lasha Abzianidze, Johan Bos
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Multi-Task Learning, Natural Language Inference, Part-Of-Speech Tagging

We investigate the effects of multi-task learning using the recently
introduced task of semantic tagging. We employ semantic tagging as an auxiliary
task for three different NLP tasks: part-of-speech tagging, Universal
Dependency parsing, and Natural Language Inference. We compare full neural
network sharing, partial neural network sharing, and what we term the learning
what to share setting where negative transfer between tasks is less likely. Our
findings show considerable improvements for all tasks, particularly in the
learning what to share setting, which shows consistent gains across all tasks.
Camera-based Image Forgery Localization using Convolutional Neural Networks
Authors: Davide Cozzolino, Luisa Verdoliva
Date: 2018-08-29 00:00:00
Tasks: Image Forensics

Camera fingerprints are precious tools for a number of image forensics tasks.
A well-known example is the photo response non-uniformity (PRNU) noise pattern,
a powerful device fingerprint. Here, to address the image forgery localization
problem, we rely on noiseprint, a recently proposed CNN-based camera model
fingerprint. The CNN is trained to minimize the distance between same-model
patches, and maximize the distance otherwise. As a result, the noiseprint
accounts for model-related artifacts just like the PRNU accounts for
device-related non-uniformities. However, unlike the PRNU, it is only mildly
affected by residuals of high-level scene content. The experiments show that
the proposed noiseprint-based forgery localization method improves over the
PRNU-based reference.
Fractional Multiscale Fusion-based De-hazing
Authors: Uche A. Nnolim
Date: 2018-08-29 00:00:00
Tasks: Image Enhancement

This report presents the results of a proposed multi-scale fusion-based
single image de-hazing algorithm, which can also be used for underwater image
enhancement. Furthermore, the algorithm was designed for very fast operation
and minimal run-time. The proposed scheme is the faster than existing
algorithms for both de-hazing and underwater image enhancement and amenable to
digital hardware implementation. Results indicate mostly consistent and good
results for both categories of images when compared with other algorithms from
the literature.
An Operation Sequence Model for Explainable Neural Machine Translation
Authors: Felix Stahlberg, Danielle Saunders, Bill Byrne
Date: 2018-08-29 00:00:00
Proceeding: WS 2018 11
Tasks: Machine Translation, NMT, Sentence, Translation, Word Alignment

We propose to achieve explainable neural machine translation (NMT) by
changing the output representation to explain itself. We present a novel
approach to NMT which generates the target sentence by monotonically walking
through the source sentence. Word reordering is modeled by operations which
allow setting markers in the target sentence and move a target-side write head
between those markers. In contrast to many modern neural models, our system
emits explicit word alignment information which is often crucial to practical
machine translation as it improves explainability. Our technique can outperform
a plain text system in terms of BLEU score under the recent Transformer
architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU
difference on Spanish-English.