pwc-archive/papers-with-abstracts huggingface dataset:
Using $k$-way Co-occurrences for Learning Word Embeddings
Authors: Danushka Bollegala, Yuichi Yoshida, Ken-ichi Kawarabayashi
Date: 2017-09-05 00:00:00
Tasks: Learning Word Embeddings, Word Embeddings

Co-occurrences between two words provide useful insights into the semantics
of those words. Consequently, numerous prior work on word embedding learning
have used co-occurrences between two words as the training signal for learning
word embeddings. However, in natural language texts it is common for multiple
words to be related and co-occurring in the same context. We extend the notion
of co-occurrences to cover $k(\geq\!\!2)$-way co-occurrences among a set of
$k$-words. Specifically, we prove a theoretical relationship between the joint
probability of $k(\geq\!\!2)$ words, and the sum of $\ell_2$ norms of their
embeddings. Next, we propose a learning objective motivated by our theoretical
result that utilises $k$-way co-occurrences for learning word embeddings. Our
experimental results show that the derived theoretical relationship does indeed
hold empirically, and despite data sparsity, for some smaller $k$ values,
$k$-way embeddings perform comparably or better than $2$-way embeddings in a
range of tasks.
Compositional Approaches for Representing Relations Between Words: A Comparative Study
Authors: Huda Hakami, Danushka Bollegala
Date: 2017-09-04 00:00:00
Tasks: Knowledge Base Completion, Relation

Identifying the relations that exist between words (or entities) is important
for various natural language processing tasks such as, relational search,
noun-modifier classification and analogy detection. A popular approach to
represent the relations between a pair of words is to extract the patterns in
which the words co-occur with from a corpus, and assign each word-pair a vector
of pattern frequencies. Despite the simplicity of this approach, it suffers
from data sparseness, information scalability and linguistic creativity as the
model is unable to handle previously unseen word pairs in a corpus. In
contrast, a compositional approach for representing relations between words
overcomes these issues by using the attributes of each individual word to
indirectly compose a representation for the common relations that hold between
the two words. This study aims to compare different operations for creating
relation representations from word-level representations. We investigate the
performance of the compositional methods by measuring the relational
similarities using several benchmark datasets for word analogy. Moreover, we
evaluate the different relation representations in a knowledge base completion
task.
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Authors: Fan Yang, Arjun Mukherjee, Eduard Dragut
Date: 2017-09-04 00:00:00
Proceeding: EMNLP 2017 9

Satirical news is considered to be entertainment, but it is potentially
deceptive and harmful. Despite the embedded genre in the article, not everyone
can recognize the satirical cues and therefore believe the news as true news.
We observe that satirical cues are often reflected in certain paragraphs rather
than the whole document. Existing works only consider document-level features
to detect the satire, which could be limited. We consider paragraph-level
linguistic features to unveil the satire by incorporating neural network and
attention mechanism. We investigate the difference between paragraph-level
features and document-level features, and analyze them on a large satirical
news dataset. The evaluation shows that the proposed model detects satirical
news effectively and reveals what features are important at which level.
Storytelling Agents with Personality and Adaptivity
Authors: Zhichao Hu, Marilyn A. Walker, Michael Neff, Jean E. Fox Tree
Date: 2017-09-04 00:00:00

We explore the expression of personality and adaptivity through the gestures
of virtual agents in a storytelling task. We conduct two experiments using four
different dialogic stories. We manipulate agent personality on the extraversion
scale, whether the agents adapt to one another in their gestural performance
and agent gender. Our results show that subjects are able to perceive the
intended variation in extraversion between different virtual agents,
independently of the story they are telling and the gender of the agent. A
second study shows that subjects also prefer adaptive to nonadaptive virtual
agents.
Learning Neural Word Salience Scores
Authors: Krasen Samardzhiev, Andrew Gargett, Danushka Bollegala
Date: 2017-09-04 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentence, Sentence Similarity, Word Embeddings

Measuring the salience of a word is an essential step in numerous NLP tasks.
Heuristic approaches such as tfidf have been used so far to estimate the
salience of words. We propose \emph{Neural Word Salience} (NWS) scores, unlike
heuristics, are learnt from a corpus. Specifically, we learn word salience
scores such that, using pre-trained word embeddings as the input, can
accurately predict the words that appear in a sentence, given the words that
appear in the sentences preceding or succeeding that sentence. Experimental
results on sentence similarity prediction show that the learnt word salience
scores perform comparably or better than some of the state-of-the-art
approaches for representing sentences on benchmark datasets for sentence
similarity, while using only a fraction of the training and prediction times
required by prior methods. Moreover, our NWS scores positively correlate with
psycholinguistic measures such as concreteness, and imageability implying a
close connection to the salience as perceived by humans.
Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements
Authors: Carlos E. Thomaz, Vagner Amaral, Gilson A. Giraldi, Duncan F. Gillies, Daniel Rueckert
Date: 2017-09-04 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Research on human face processing using eye movements has provided evidence
that we recognize face images successfully focusing our visual attention on a
few inner facial regions, mainly on the eyes, nose and mouth. To understand how
we accomplish this process of coding high-dimensional faces so efficiently,
this paper proposes and implements a multivariate extraction method that
combines face images variance with human spatial attention maps modeled as
feature- and pattern-based information sources. It is based on a unified
multidimensional representation of the well-known face-space concept. The
spatial attention maps are summary statistics of the eye-tracking fixations of
a number of participants and trials to frontal and well-framed face images
during separate gender and facial expression recognition tasks. Our
experimental results carried out on publicly available face databases have
indicated that we might emulate the human extraction system as a pattern-based
computational method rather than a feature-based one to properly explain the
proficiency of the human system in recognizing visual face information.
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
Authors: Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin
Date: 2017-09-04 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as
a flexible family of scalable Bayesian sampling algorithms. However, there has
been little theoretical analysis of the impact of minibatch size to the
algorithm's convergence rate. In this paper, we prove that under a limited
computational budget/time, a larger minibatch size leads to a faster decrease
of the mean squared error bound (thus the fastest one corresponds to using full
gradients), which motivates the necessity of variance reduction in SG-MCMC.
Consequently, by borrowing ideas from stochastic optimization, we propose a
practical variance-reduction technique for SG-MCMC, that is efficient in both
computation and storage. We develop theory to prove that our algorithm induces
a faster convergence rate than standard SG-MCMC. A number of large-scale
experiments, ranging from Bayesian learning of logistic regression to deep
neural networks, validate the theory and demonstrate the superiority of the
proposed variance-reduction SG-MCMC framework.
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
Authors: Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal
Date: 2017-09-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Zero-Shot Learning

In this paper, we study learning visual classifiers from unstructured text
descriptions at part precision with no training images. We propose a learning
framework that is able to connect text terms to its relevant parts and suppress
connections to non-visual text terms without any part-text annotations. For
instance, this learning process enables terms like "beak" to be sparsely linked
to the visual representation of parts like head, while reduces the effect of
non-visual terms like "migrate" on classifier prediction. Images are encoded by
a part-based CNN that detect bird parts and learn part-specific representation.
Part-based visual classifiers are predicted from text descriptions of unseen
visual classifiers to facilitate classification without training images (also
known as zero-shot recognition). We performed our experiments on CUBirds 2011
dataset and improves the state-of-the-art text-based zero-shot recognition
results from 34.7\% to 43.6\%. We also created large scale benchmarks on North
American Bird Images augmented with text descriptions, where we also show that
our approach outperforms existing methods. Our code, data, and models are
publically available.
Balancing Interpretability and Predictive Accuracy for Unsupervised Tensor Mining
Authors: Ishmam Zabir, Evangelos E. Papalexakis
Date: 2017-09-04 00:00:00
Tasks: Diagnostic, Tensor Decomposition

The PARAFAC tensor decomposition has enjoyed an increasing success in
exploratory multi-aspect data mining scenarios. A major challenge remains the
estimation of the number of latent factors (i.e., the rank) of the
decomposition, which yields high-quality, interpretable results. Previously, we
have proposed an automated tensor mining method which leverages a well-known
quality heuristic from the field of Chemometrics, the Core Consistency
Diagnostic (CORCONDIA), in order to automatically determine the rank for the
PARAFAC decomposition. In this work we set out to explore the trade-off between
1) the interpretability/quality of the results (as expressed by CORCONDIA), and
2) the predictive accuracy of the results, in order to further improve the rank
estimation quality. Our preliminary results indicate that striking a good
balance in that trade-off benefits rank estimation.
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
Authors: Yizhe Zhu, Ahmed Elgammal
Date: 2017-09-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation

The exponentially increasing use of moving platforms for video capture
introduces the urgent need to develop the general background subtraction
algorithms with the capability to deal with the moving background. In this
paper, we propose a multilayer-based framework for online background
subtraction for videos captured by moving cameras. Unlike the previous
treatments of the problem, the proposed method is not restricted to binary
segmentation of background and foreground, but formulates it as a multi-label
segmentation problem by modeling multiple foreground objects in different
layers when they appear simultaneously in the scene. We assign an independent
processing layer to each foreground object, as well as the background, where
both motion and appearance models are estimated, and a probability map is
inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut
on Markov Random Field is employed to perform pixel-wise labeling. Extensive
evaluation results show that the proposed method outperforms state-of-the-art
methods on challenging video sequences.
WRPN: Wide Reduced-Precision Networks
Authors: Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr
Date: 2017-09-04 00:00:00
Proceeding: ICLR 2018 1

For computer vision applications, prior works have shown the efficacy of
reducing numeric precision of model parameters (network weights) in deep neural
networks. Activation maps, however, occupy a large memory footprint during both
the training and inference step when using mini-batches of inputs. One way to
reduce this large memory footprint is to reduce the precision of activations.
However, past works have shown that reducing the precision of activations hurts
model accuracy. We study schemes to train networks from scratch using
reduced-precision activations without hurting accuracy. We reduce the precision
of activation maps (along with model parameters) and increase the number of
filter maps in a layer, and find that this scheme matches or surpasses the
accuracy of the baseline full-precision network. As a result, one can
significantly improve the execution efficiency (e.g. reduce dynamic memory
footprint, memory bandwidth and computational energy) and speed up the training
and inference process with appropriate hardware support. We call our scheme
WRPN - wide reduced-precision networks. We report results and show that WRPN
scheme is better than previously reported accuracies on ILSVRC-12 dataset while
being computationally less expensive compared to previously reported
reduced-precision networks.
Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals
Authors: Daniel Lopez-Martinez, Rosalind Picard
Date: 2017-08-17 00:00:00
Tasks: Multi-Task Learning

Pain is a complex and subjective experience that poses a number of
measurement challenges. While self-report by the patient is viewed as the gold
standard of pain assessment, this approach fails when patients cannot verbally
communicate pain intensity or lack normal mental abilities. Here, we present a
pain intensity measurement method based on physiological signals. Specifically,
we implement a multi-task learning approach based on neural networks that
accounts for individual differences in pain responses while still leveraging
data from across the population. We test our method in a dataset containing
multi-modal physiological responses to nociceptive pain.
Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories
Authors: Rodrigo de Salvo Braz, Ciaran O'Reilly
Date: 2017-09-04 00:00:00
Tasks: 10-shot image generation, 3D-Aware Image Synthesis

Probabilistic Inference Modulo Theories (PIMT) is a recent framework that
expands exact inference on graphical models to use richer languages that
include arithmetic, equalities, and inequalities on both integers and real
numbers. In this paper, we expand PIMT to a lifted version that also processes
random functions and relations. This enhancement is achieved by adapting
Inversion, a method from Lifted First-Order Probabilistic Inference literature,
to also be modulo theories. This results in the first algorithm for exact
probabilistic inference that efficiently and simultaneously exploits random
relations and functions, arithmetic, equalities and inequalities.
The Pragmatics of Indirect Commands in Collaborative Discourse
Authors: Matthew Lamm, Mihail Eric
Date: 2017-05-08 00:00:00
Proceeding: WS 2017 1

Today's artificial assistants are typically prompted to perform tasks through
direct, imperative commands such as \emph{Set a timer} or \emph{Pick up the
box}. However, to progress toward more natural exchanges between humans and
these assistants, it is important to understand the way non-imperative
utterances can indirectly elicit action of an addressee. In this paper, we
investigate command types in the setting of a grounded, collaborative game. We
focus on a less understood family of utterances for eliciting agent action,
locatives like \emph{The chair is in the other room}, and demonstrate how these
utterances indirectly command in specific game state contexts. Our work shows
that models with domain-specific grounding can effectively realize the
pragmatic reasoning that is necessary for more robust natural language
interaction.
"I can assure you [$\ldots$] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
Authors: Brett W. Israelsen
Date: 2017-08-01 00:00:00
Tasks: All, Survey

As technology become more advanced, those who design, use and are otherwise
affected by it want to know that it will perform correctly, and understand why
it does what it does, and how to use it appropriately. In essence they want to
be able to trust the systems that are being designed. In this survey we present
assurances that are the method by which users can understand how to trust this
technology. Trust between humans and autonomy is reviewed, and the implications
for the design of assurances are highlighted. A survey of research that has
been performed with respect to assurances is presented, and several key ideas
are extracted in order to refine the definition of assurances. Several
directions for future research are identified and discussed.
A Unified Query-based Generative Model for Question Generation and Question Answering
Authors: Linfeng Song, Zhiguo Wang, Wael Hamza
Date: 2017-09-04 00:00:00
Tasks: Decoder, Question Answering, Question Generation, Question-Generation, Reinforcement Learning

We propose a query-based generative model for solving both tasks of question
generation (QG) and question an- swering (QA). The model follows the classic
encoder- decoder framework. The encoder takes a passage and a query as input
then performs query understanding by matching the query with the passage from
multiple per- spectives. The decoder is an attention-based Long Short Term
Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a
question is generated from the system given the passage and the target answer,
whereas in the QA task, the answer is generated given the question and the
passage. During the training stage, we leverage a policy-gradient reinforcement
learning algorithm to overcome exposure bias, a major prob- lem resulted from
sequence learning with cross-entropy loss. For the QG task, our experiments
show higher per- formances than the state-of-the-art results. When used as
additional training data, the automatically generated questions even improve
the performance of a strong ex- tractive QA system. In addition, our model
shows bet- ter performance than the state-of-the-art baselines of the
generative QA task.
LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution
Authors: Yvain Quéau, Bastien Durix, Tao Wu, Daniel Cremers, François Lauze, Jean-Denis Durou
Date: 2017-07-04 00:00:00

We conduct a thorough study of photometric stereo under nearby point light
source illumination, from modeling to numerical solution, through calibration.
In the classical formulation of photometric stereo, the luminous fluxes are
assumed to be directional, which is very difficult to achieve in practice.
Rather, we use light-emitting diodes (LEDs) to illuminate the scene to
reconstruct. Such point light sources are very convenient to use, yet they
yield a more complex photometric stereo model which is arduous to solve. We
first derive in a physically sound manner this model, and show how to calibrate
its parameters. Then, we discuss two state-of-the-art numerical solutions. The
first one alternatingly estimates the albedo and the normals, and then
integrates the normals into a depth map. It is shown empirically to be
independent from the initialization, but convergence of this sequential
approach is not established. The second one directly recovers the depth, by
formulating photometric stereo as a system of PDEs which are partially
linearized using image ratios. Although the sequential approach is avoided,
initialization matters a lot and convergence is not established either.
Therefore, we introduce a provably convergent alternating reweighted
least-squares scheme for solving the original system of PDEs, without resorting
to image ratios for linearization. Finally, we extend this study to the case of
RGB images.
A Nonparametric Model for Multimodal Collaborative Activities Summarization
Authors: Guy Rosman, John W. Fisher III, Daniela Rus
Date: 2017-09-04 00:00:00
Tasks: Action Detection, Activity Detection

Ego-centric data streams provide a unique opportunity to reason about joint
behavior by pooling data across individuals. This is especially evident in
urban environments teeming with human activities, but which suffer from
incomplete and noisy data. Collaborative human activities exhibit common
spatial, temporal, and visual characteristics facilitating inference across
individuals from multiple sensory modalities as we explore in this paper from
the perspective of meetings. We propose a new Bayesian nonparametric model that
enables us to efficiently pool video and GPS data towards collaborative
activities analysis from multiple individuals. We demonstrate the utility of
this model for inference tasks such as activity detection, classification, and
summarization. We further demonstrate how spatio-temporal structure embedded in
our model enables better understanding of partial and noisy observations such
as localization and face detections based on social interactions. We show
results on both synthetic experiments and a new dataset of egocentric video and
noisy GPS data from multiple individuals.
Getting Reliable Annotations for Sarcasm in Online Dialogues
Authors: Reid Swanson, Stephanie Lukin, Luke Eisenberg, Thomas Chase Corcoran, Marilyn A. Walker
Date: 2017-09-04 00:00:00
Proceeding: LREC 2014 5

The language used in online forums differs in many ways from that of
traditional language resources such as news. One difference is the use and
frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the
aim is to develop a theory of sarcasm in dialogue, or engineer automatic
methods for reliably detecting sarcasm, a major challenge is simply the
difficulty of getting enough reliably labelled examples. In this paper we
describe our work on methods for achieving highly reliable sarcasm annotations
from untrained annotators on Mechanical Turk. We explore the use of a number of
common statistical reliability measures, such as Kappa, Karger's, Majority
Class, and EM. We show that more sophisticated measures do not appear to yield
better results for our data than simple measures such as assuming that the
correct label is the one that a majority of Turkers apply.