pwc-archive/papers-with-abstracts huggingface dataset:
DeFactoNLP: Fact Verification using Entity Recognition, TFIDF Vector Comparison and Decomposable Attention
Authors: Aniketh Janardhan Reddy, Gil Rocha, Diego Esteves
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Fact Verification, Natural Language Inference, Sentence

In this paper, we describe DeFactoNLP, the system we designed for the FEVER
2018 Shared Task. The aim of this task was to conceive a system that can not
only automatically assess the veracity of a claim but also retrieve evidence
supporting this assessment from Wikipedia. In our approach, the Wikipedia
documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are
most similar to the vector of the claim and those documents whose names are
similar to those of the named entities (NEs) mentioned in the claim are
identified as the documents which might contain evidence. The sentences in
these documents are then supplied to a textual entailment recognition module.
This module calculates the probability of each sentence supporting the claim,
contradicting the claim or not providing any relevant information to assess the
veracity of the claim. Various features computed using these probabilities are
finally used by a Random Forest classifier to determine the overall
truthfulness of the claim. The sentences which support this classification are
returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a
0.5136 label accuracy and a 0.3833 FEVER score.
LRS3-TED: a large-scale dataset for visual speech recognition
Authors: Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
Date: 2018-09-03 00:00:00
Tasks: Audio-Visual Speech Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition, Word Alignment

This paper introduces a new multi-modal dataset for visual and audio-visual
speech recognition. It includes face tracks from over 400 hours of TED and TEDx
videos, along with the corresponding subtitles and word alignment boundaries.
The new dataset is substantially larger in scale compared to other public
datasets that are available for general research.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
Authors: Diego Esteves, Aniketh Janardhan Reddy, Piyush Chawla, Jens Lehmann
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Fact Checking, Fake News Detection, Subjectivity Analysis, Web Credibility

With the growth of the internet, the number of fake-news online has been
proliferating every year. The consequences of such phenomena are manifold,
ranging from lousy decision-making process to bullying and violence episodes.
Therefore, fact-checking algorithms became a valuable asset. To this aim, an
important step to detect fake-news is to have access to a credibility score for
a given information source. However, most of the widely used Web indicators
have either been shut-down to the public (e.g., Google PageRank) or are not
free for use (Alexa Rank). Further existing databases are short-manually
curated lists of online sources, which do not scale. Finally, most of the
research on the topic is theoretical-based or explore confidential data in a
restricted simulation environment. In this paper we explore current research,
highlight the challenges and propose solutions to tackle the problem of
classifying websites into a credibility scale. The proposed model automatically
extracts source reputation cues and computes a credibility factor, providing
valuable insights which can help in belittling dubious and confirming trustful
unknown websites. Experimental results outperform state of the art in the
2-classes and 5-classes setting.
Prediction of Electric Multiple Unit Fleet Size Based on Convolutional Neural Network
Authors: Boliang Lin
Date: 2018-09-03 00:00:00

With the expansion of high-speed railway network and growth of passenger
transportation demands, the fleet size of electric multiple unit (EMU) in China
needs to be adjusted accordingly. Generally, an EMU train costs tens of
millions of dollars which constitutes a significant portion of capital
investment. Thus, the prediction of EMU fleet size has attracted increasing
attention from associated railway departments. First, this paper introduces a
typical architecture of convolutional neural network (CNN) and its basic
theory. Then, some data of nine indices, such as passenger traffic volume and
length of high-speed railways in operation, is collected and preprocessed.
Next, a CNN and a backpropagation neural network (BPNN) are constructed and
trained aiming to predict EMU fleet size in the following years. The
differences and performances of these two networks in computation experiments
are analyzed in-depth. The results indicate that the CNN is superior to the
BPNN both in generalization ability and fitting accuracy, and CNN can serve as
an aid in EMU fleet size prediction.
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
Authors: Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen, Mujdat Cetin
Date: 2018-09-03 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper, we propose an efficient pseudo-marginal Markov chain Monte
Carlo (MCMC) sampling approach to draw samples from posterior shape
distributions for image segmentation. The computation time of the proposed
approach is independent from the size of the training set used to learn the
shape prior distribution nonparametrically. Therefore, it scales well for very
large data sets. Our approach is able to characterize the posterior probability
density in the space of shapes through its samples, and to return multiple
solutions, potentially from different modes of a multimodal probability
density, which would be encountered, e.g., in segmenting objects from multiple
shape classes. Experimental results demonstrate the potential of the proposed
approach.
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Authors: Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
Date: 2018-09-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Segmentation, Object, One-shot visual object segmentation, Optical Flow Estimation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation, Visual Object Tracking

Learning long-term spatial-temporal features are critical for many video
analysis tasks. However, existing video segmentation methods predominantly rely
on static image segmentation techniques, and methods capturing temporal
dependency for segmentation have to depend on pretrained optical flow models,
leading to suboptimal solutions for the problem. End-to-end sequential learning
to explore spatial-temporal features for video segmentation is largely limited
by the scale of available video segmentation datasets, i.e., even the largest
video segmentation dataset only contains 90 short video clips. To solve this
problem, we build a new large-scale video object segmentation dataset called
YouTube Video Object Segmentation dataset (YouTube-VOS). Our dataset contains
3,252 YouTube video clips and 78 categories including common objects and human
activities. This is by far the largest video object segmentation dataset to our
knowledge and we have released it at https://youtube-vos.org. Based on this
dataset, we propose a novel sequence-to-sequence network to fully exploit
long-term spatial-temporal information in videos for segmentation. We
demonstrate that our method is able to achieve the best results on our
YouTube-VOS test set and comparable results on DAVIS 2016 compared to the
current state-of-the-art methods. Experiments show that the large scale dataset
is indeed a key factor to the success of our model.
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Authors: Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin
Date: 2018-09-03 00:00:00
Tasks: Image Super-Resolution, Image-to-Image Translation, Super-Resolution, Translation

We consider the single image super-resolution problem in a more general case
that the low-/high-resolution pairs and the down-sampling process are
unavailable. Different from traditional super-resolution formulation, the
low-resolution input is further degraded by noises and blurring. This
complicated setting makes supervised learning and accurate kernel estimation
impossible. To solve this problem, we resort to unsupervised learning without
paired data, inspired by the recent successful image-to-image translation
applications. With generative adversarial networks (GAN) as the basic
component, we propose a Cycle-in-Cycle network structure to tackle the problem
within three steps. First, the noisy and blurry input is mapped to a noise-free
low-resolution space. Then the intermediate image is up-sampled with a
pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end
manner to get the high-resolution output. Experiments on NTIRE2018 datasets
demonstrate that the proposed unsupervised method achieves comparable results
as the state-of-the-art supervised models.
Data Augmentation for Neural Online Chat Response Selection
Authors: Wenchao Du, Alan W. black
Date: 2018-09-03 00:00:00
Tasks: Data Augmentation

Data augmentation seeks to manipulate the available data for training to
improve the generalization ability of models. We investigate two data
augmentation proxies, permutation and flipping, for neural dialog response
selection task on various models over multiple datasets, including both Chinese
and English languages. Different from standard data augmentation techniques,
our method combines the original and synthesized data for prediction. Empirical
results show that our approach can gain 1 to 3 recall-at-1 points over baseline
models in both full-scale and small-scale settings.
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition
Authors: Yang Liu, Zhaoyang Lu, Jing Li, Tao Yang
Date: 2018-09-03 00:00:00
Tasks: Action Recognition, Denoising, Temporal Action Localization

Recognizing human actions from varied views is challenging due to huge appearance variations in different views. The key to this problem is to learn discriminant view-invariant representations generalizing well across views. In this paper, we address this problem by learning view-invariant representations hierarchically using a novel method, referred to as Joint Sparse Representation and Distribution Adaptation (JSRDA). To obtain robust and informative feature representations, we first incorporate a sample-affinity matrix into the marginalized stacked denoising Autoencoder (mSDA) to obtain shared features, which are then combined with the private features. In order to make the feature representations of videos across views transferable, we then learn a transferable dictionary pair simultaneously from pairs of videos taken at different views to encourage each action video across views to have the same sparse representation. However, the distribution difference across views still exists because a unified subspace where the sparse representations of one action across views are the same may not exist when the view difference is large. Therefore, we propose a novel unsupervised distribution adaptation method that learns a set of projections that project the source and target views data into respective low-dimensional subspaces where the marginal and conditional distribution differences are reduced simultaneously. Therefore, the finally learned feature representation is view-invariant and robust for substantial distribution difference across views even the view difference is large. Experimental results on four multiview datasets show that our approach outperforms the state-ofthe-art approaches.
Hypernyms Through Intra-Article Organization in Wikipedia
Authors: Disha Shrivastava, Sreyash Kenkre, Santosh Penubothula
Date: 2018-09-03 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity

We introduce a new measure for unsupervised hypernym detection and
directionality. The motivation is to keep the measure computationally light and
portatable across languages. We show that the relative physical location of
words in explanatory articles captures the directionality property. Further,
the phrases in section titles of articles about the word, capture the semantic
similarity needed for hypernym detection task. We experimentally show that the
combination of features coming from these two simple measures suffices to
produce results comparable with the best unsupervised measures in terms of the
average precision.
Modeling Topical Coherence in Discourse without Supervision
Authors: Disha Shrivastava, Abhijit Mishra, Karthik Sankaranarayanan
Date: 2018-09-02 00:00:00
Tasks: Attribute

Coherence of text is an important attribute to be measured for both manually
and automatically generated discourse; but well-defined quantitative metrics
for it are still elusive. In this paper, we present a metric for scoring
topical coherence of an input paragraph on a real-valued scale by analyzing its
underlying topical structure. We first extract all possible topics that the
sentences of a paragraph of text are related to. Coherence of this text is then
measured by computing: (a) the degree of uncertainty of the topics with respect
to the paragraph, and (b) the relatedness between these topics. All components
of our modular framework rely only on unlabeled data and WordNet, thus making
it completely unsupervised, which is an important feature for general-purpose
usage of any metric. Experiments are conducted on two datasets - a publicly
available dataset for essay grading (representing human discourse), and a
synthetic dataset constructed by mixing content from multiple paragraphs
covering diverse topics. Our evaluation shows that the measured coherence
scores are positively correlated with the ground truth for both the datasets.
Further validation to our coherence scores is provided by conducting human
evaluation on the synthetic data, showing a significant agreement of 79.3%
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams
Authors: Alejandro Cartas, Estefania Talavera, Petia Radeva, Mariella Dimiccoli
Date: 2018-09-02 00:00:00
Tasks: Action Recognition, Activity Recognition, Egocentric Activity Recognition, Temporal Action Localization

Event boundaries play a crucial role as a pre-processing step for detection,
localization, and recognition tasks of human activities in videos. Typically,
although their intrinsic subjectiveness, temporal bounds are provided manually
as input for training action recognition algorithms. However, their role for
activity recognition in the domain of egocentric photostreams has been so far
neglected. In this paper, we provide insights of how automatically computed
boundaries can impact activity recognition results in the emerging domain of
egocentric photostreams. Furthermore, we collected a new annotated dataset
acquired by 15 people by a wearable photo-camera and we used it to show the
generalization capabilities of several deep learning based architectures to
unseen users.
Visual Transfer between Atari Games using Competitive Reinforcement Learning
Authors: Akshita Mittel, Sowmya Munukutla, Himanshi Yadav
Date: 2018-09-02 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper explores the use of deep reinforcement learning agents to transfer
knowledge from one environment to another. More specifically, the method takes
advantage of asynchronous advantage actor critic (A3C) architecture to
generalize a target game using an agent trained on a source game in Atari.
Instead of fine-tuning a pre-trained model for the target game, we propose a
learning approach to update the model using multiple agents trained in parallel
with different representations of the target game. Visual mapping between video
sequences of transfer pairs is used to derive new representations of the target
game; training on these visual representations of the target game improves
model updates in terms of performance, data efficiency and stability. In order
to demonstrate the functionality of the architecture, Atari games Pong-v0 and
Breakout-v0 are being used from the OpenAI gym environment; as the source and
target environment.
MTNT: A Testbed for Machine Translation of Noisy Text
Authors: Paul Michel, Graham Neubig
Date: 2018-09-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

Noisy or non-standard input text can cause disastrous mistranslations in most
modern Machine Translation (MT) systems, and there has been growing research
interest in creating noise-robust MT systems. However, as of yet there are no
publicly available parallel corpora of with naturally occurring noisy inputs
and translations, and thus previous work has resorted to evaluating on
synthetically created datasets. In this paper, we propose a benchmark dataset
for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on
Reddit (www.reddit.com) and professionally sourced translations. We
commissioned translations of English comments into French and Japanese, as well
as French and Japanese comments into English, on the order of 7k-37k sentences
per language pair. We qualitatively and quantitatively examine the types of
noise included in this dataset, then demonstrate that existing MT models fail
badly on a number of noise-related phenomena, even after performing adaptation
on a small training set of in-domain data. This indicates that this dataset can
provide an attractive testbed for methods tailored to handling noisy text in
MT. The data is publicly available at www.cs.cmu.edu/~pmichel1/mtnt/.
Zero-shot User Intent Detection via Capsule Neural Networks
Authors: Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu
Date: 2018-09-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Intent Detection, Question Answering, Transfer Learning, Zero-Shot Learning

User intent detection plays a critical role in question-answering and dialog
systems. Most previous works treat intent detection as a classification problem
where utterances are labeled with predefined intents. However, it is
labor-intensive and time-consuming to label users' utterances as intents are
diversely expressed and novel intents will continually be involved. Instead, we
study the zero-shot intent detection problem, which aims to detect emerging
user intents where no labeled utterances are currently available. We propose
two capsule-based architectures: INTENT-CAPSNET that extracts semantic features
from utterances and aggregates them to discriminate existing intents, and
INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to
discriminate emerging intents via knowledge transfer from existing intents.
Experiments on two real-world datasets show that our model not only can better
discriminate diversely expressed existing intents, but is also able to
discriminate emerging intents when no labeled utterances are available.
Multitask Learning for Fundamental Frequency Estimation in Music
Authors: Rachel M. Bittner, Brian McFee, Juan P. Bello
Date: 2018-09-02 00:00:00

Fundamental frequency (f0) estimation from polyphonic music includes the
tasks of multiple-f0, melody, vocal, and bass line estimation. Historically
these problems have been approached separately, and only recently, using
learning-based approaches. We present a multitask deep learning architecture
that jointly estimates outputs for various tasks including multiple-f0, melody,
vocal and bass line estimation, and is trained using a large,
semi-automatically annotated dataset. We show that the multitask model
outperforms its single-task counterparts, and explore the effect of various
design decisions in our approach, and show that it performs better or at least
competitively when compared against strong baseline methods.
Neural Character-based Composition Models for Abuse Detection
Authors: Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova
Date: 2018-09-02 00:00:00
Proceeding: WS 2018 10
Tasks: Abuse Detection, Abusive Language

The advent of social media in recent years has fed into some highly
undesirable phenomena such as proliferation of offensive language, hate speech,
sexist remarks, etc. on the Internet. In light of this, there have been several
efforts to automate the detection and moderation of such abusive content.
However, deliberate obfuscation of words by users to evade detection poses a
serious challenge to the effectiveness of these efforts. The current state of
the art approaches to abusive language detection, based on recurrent neural
networks, do not explicitly address this problem and resort to a generic OOV
(out of vocabulary) embedding for unseen words. However, in using a single
embedding for all unseen words we lose the ability to distinguish between
obfuscated and non-obfuscated or rare words. In this paper, we address this
problem by designing a model that can compose embeddings for unseen words. We
experimentally demonstrate that our approach significantly advances the current
state of the art in abuse detection on datasets from two different domains,
namely Twitter and Wikipedia talk page.
Neural Ranking Models for Temporal Dependency Structure Parsing
Authors: Yuchen Zhang, Nianwen Xue
Date: 2018-09-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering

We design and build the first neural temporal dependency parser. It utilizes
a neural ranking model with minimal feature engineering, and parses time
expressions and events in a text into a temporal dependency tree structure. We
evaluate our parser on two domains: news reports and narrative stories. In a
parsing-only evaluation setup where gold time expressions and events are
provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled
parsing respectively, a result that is very competitive against alternative
approaches. In an end-to-end evaluation setup where time expressions and events
are automatically recognized, our parser beats two strong baselines on both
data domains. Our experimental results and discussions shed light on the nature
of temporal dependency structures in different domains and provide insights
that we believe will be valuable to future research in this area.
Overcoming the Curse of Dimensionality in Neural Networks
Authors: Karen Yeressian
Date: 2018-09-02 00:00:00

Let $A$ be a set and $V$ a real Hilbert space. Let $H$ be a real Hilbert space of functions $f:A\to V$ and assume $H$ is continuously embedded in the Banach space of bounded functions. For $i=1,\cdots,n$, let $(x_i,y_i)\in A\times V$ comprise our dataset. Let $0<q<1$ and $f^*\in H$ be the unique global minimizer of the functional \begin{equation*} u(f) = \frac{q}{2}\Vert f\Vert_{H}^{2} + \frac{1-q}{2n}\sum_{i=1}^{n}\Vert f(x_i)-y_i\Vert_{V}^{2}. \end{equation*} In this paper we show that for each $k\in\mathbb{N}$ there exists a two layer network where the first layer has $k$ functions which are Riesz representations in the Hilbert space $H$ of point evaluation functionals and the second layer is a weighted sum of the first layer, such that the functions $f_k$ realized by these networks satisfy \begin{equation*} \Vert f_{k}-f^*\Vert_{H}^{2} \leq \Bigl( o(1) + \frac{C}{q^2} E\bigl[ \Vert Du_{I}(f^*)\Vert_{H^{*}}^{2} \bigr] \Bigr)\frac{1}{k}. \end{equation*} %Let us note that $x_i$ do not need to be in a linear space and $y_i$ are in a possibly infinite dimensional Hilbert space $V$. %The error estimate is independent of the data size $n$ and in the case $V$ is finite dimensional %the error estimate is also independent of the dimension of $V$. By choosing the Hilbert space $H$ appropriately, the computational complexity of evaluating the Riesz representations of point evaluations might be small and thus the network has low computational complexity.