pwc-archive/papers-with-abstracts huggingface dataset:
One-Shot Relational Learning for Knowledge Graphs
Authors: Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Knowledge Graph Completion, Knowledge Graphs, Relational Reasoning

Knowledge graphs (KGs) are the key components of various natural language
processing applications. To further expand KGs' coverage, previous studies on
knowledge graph completion usually require a large number of training instances
for each relation. However, we observe that long-tail relations are actually
more common in KGs and those newly added relations often do not have many known
triples for training. In this work, we aim at predicting new facts under a
challenging setting where only one training instance is available. We propose a
one-shot relational learning framework, which utilizes the knowledge extracted
by embedding models and learns a matching metric by considering both the
learned embeddings and one-hop graph structures. Empirically, our model yields
considerable performance improvements over existing embedding models, and also
eliminates the need of re-training the embedding models when dealing with newly
added relations.
Measuring the Volatility of the Political agenda in Public Opinion and News Media
Authors: Chico Q. Camargo, Scott A. Hale, Peter John, Helen Z. Margetts
Date: 2018-08-27 00:00:00

Recent election surprises, regime changes, and political shocks indicate that political agendas have become more fast-moving and volatile. The ability to measure the complex dynamics of agenda change and capture the nature and extent of volatility in political systems is therefore more crucial than ever before. This study proposes a definition and operationalization of volatility that combines insights from political science, communications, information theory, and computational techniques. The proposed measures of fractionalization and agenda change encompass the shifting salience of issues in the agenda as a whole and allow the study of agendas across different domains. We evaluate these metrics and compare them to other measures such as issue-level survival rates and the Pedersen Index, which uses public-opinion poll data to measure public agendas, as well as traditional media content to measure media agendas in the UK and Germany. We show how these measures complement existing approaches and could be employed in future agenda-setting research.
Importance Weighting and Variational Inference
Authors: Justin Domke, Daniel Sheldon
Date: 2018-08-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Recent work used importance sampling ideas for better variational bounds on
likelihoods. We clarify the applicability of these ideas to pure probabilistic
inference, by showing the resulting Importance Weighted Variational Inference
(IWVI) technique is an instance of augmented variational inference, thus
identifying the looseness in previous work. Experiments confirm IWVI's
practicality for probabilistic inference. As a second contribution, we
investigate inference with elliptical distributions, which improves accuracy in
low dimensions, and convergence in high dimensions.
Targeted Syntactic Evaluation of Language Models
Authors: Rebecca Marvin, Tal Linzen
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: CCG Supertagging, Language Modeling, Language Modelling, Sentence

We present a dataset for evaluating the grammaticality of the predictions of
a language model. We automatically construct a large number of minimally
different pairs of English sentences, each consisting of a grammatical and an
ungrammatical sentence. The sentence pairs represent different variations of
structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and
negative polarity items. We expect a language model to assign a higher
probability to the grammatical sentence than the ungrammatical one. In an
experiment using this data set, an LSTM language model performed poorly on many
of the constructions. Multi-task training with a syntactic objective (CCG
supertagging) improved the LSTM's accuracy, but a large gap remained between
its performance and the accuracy of human participants recruited online. This
suggests that there is considerable room for improvement over LSTMs in
capturing syntax in a language model.
Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy
Authors: Mizanur Rahman, Mhafuzul Islam, Jon Calhoun, Mashrur Chowdhury
Date: 2018-08-27 00:00:00
Tasks: Edge-computing, object-detection, Object Detection, Pedestrian Detection

Vehicle-to-Pedestrian (V2P) communication can significantly improve
pedestrian safety at a signalized intersection. It is unlikely that pedestrians
will carry a low latency communication enabled device and activate a pedestrian
safety application in their hand-held device all the time. Because of this
limitation, multiple traffic cameras at the signalized intersection can be used
to accurately detect and locate pedestrians using deep learning and broadcast
safety alerts related to pedestrians to warn connected and automated vehicles
around a signalized intersection. However, unavailability of high-performance
computing infrastructure at the roadside and limited network bandwidth between
traffic cameras and the computing infrastructure limits the ability of
real-time data streaming and processing for pedestrian detection. In this
paper, we develop an edge computing based real-time pedestrian detection
strategy combining pedestrian detection algorithm using deep learning and an
efficient data communication approach to reduce bandwidth requirements while
maintaining a high object detection accuracy. We utilize a lossy compression
technique on traffic camera data to determine the tradeoff between the
reduction of the communication bandwidth requirements and a defined object
detection accuracy. The performance of the pedestrian-detection strategy is
measured in terms of pedestrian classification accuracy with varying peak
signal-to-noise ratios. The analyses reveal that we detect pedestrians by
maintaining a defined detection accuracy with a peak signal-to-noise ratio
(PSNR) 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to
0.31 Mbits/sec, a 31x reduction.
Review Helpfulness Assessment based on Convolutional Neural Network
Authors: Xianshan Qu, Xiaopeng Li, John R. Rose
Date: 2018-08-27 00:00:00

In this paper we describe the implementation of a convolutional neural
network (CNN) used to assess online review helpfulness. To our knowledge, this
is the first use of this architecture to address this problem. We explore the
impact of two related factors impacting CNN performance: different word
embedding initializations and different input review lengths. We also propose
an approach to combining rating star information with review text to further
improve prediction accuracy. We demonstrate that this can improve the overall
accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show
an improvement in accuracy relative to published results for traditional
methods of 2.5% for a model trained using only review text and 4.24% for a
model trained on a combination of rating star information and review text.
Natural Language Generation with Neural Variational Models
Authors: Hareesh Bahuleyan
Date: 2018-08-27 00:00:00
Tasks: Text Generation

In this thesis, we explore the use of deep neural networks for generation of
natural language. Specifically, we implement two sequence-to-sequence neural
variational models - variational autoencoders (VAE) and variational
encoder-decoders (VED). VAEs for text generation are difficult to train due to
issues associated with the Kullback-Leibler (KL) divergence term of the loss
function vanishing to zero. We successfully train VAEs by implementing
optimization heuristics such as KL weight annealing and word dropout. We also
demonstrate the effectiveness of this continuous latent space through
experiments such as random sampling, linear interpolation and sampling from the
neighborhood of the input. We argue that if VAEs are not designed
appropriately, it may lead to bypassing connections which results in the latent
space being ignored during training. We show experimentally with the example of
decoder hidden state initialization that such bypassing connections degrade the
VAE into a deterministic model, thereby reducing the diversity of generated
sentences. We discover that the traditional attention mechanism used in
sequence-to-sequence VED models serves as a bypassing connection, thereby
deteriorating the model's latent space. In order to circumvent this issue, we
propose the variational attention mechanism where the attention context vector
is modeled as a random variable that can be sampled from a distribution. We
show empirically using automatic evaluation metrics, namely entropy and
distinct measures, that our variational attention model generates more diverse
output sentences than the deterministic attention model. A qualitative analysis
with human evaluation study proves that our model simultaneously produces
sentences that are of high quality and equally fluent as the ones generated by
the deterministic attention counterpart.
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation
Authors: Marzieh Fadaee, Christof Monz
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

Neural Machine Translation has achieved state-of-the-art performance for
several language pairs using a combination of parallel and synthetic data.
Synthetic data is often generated by back-translating sentences randomly
sampled from monolingual data using a reverse translation model. While
back-translation has been shown to be very effective in many cases, it is not
entirely clear why. In this work, we explore different aspects of
back-translation, and show that words with high prediction loss during training
benefit most from the addition of synthetic data. We introduce several
variations of sampling strategies targeting difficult-to-predict words using
prediction losses and frequencies of words. In addition, we also target the
contexts of difficult words and sample sentences that are similar in context.
Experimental results for the WMT news translation task show that our method
improves translation quality by up to 1.7 and 1.2 Bleu points over
back-translation using random sampling for German-English and English-German,
respectively.
Downstream Effects of Affirmative Action
Authors: Sampath Kannan, Aaron Roth, Juba Ziani
Date: 2018-08-27 00:00:00
Tasks: Fairness, Vocal Bursts Type Prediction

We study a two-stage model, in which students are 1) admitted to college on
the basis of an entrance exam which is a noisy signal about their
qualifications (type), and then 2) those students who were admitted to college
can be hired by an employer as a function of their college grades, which are an
independently drawn noisy signal of their type. Students are drawn from one of
two populations, which might have different type distributions. We assume that
the employer at the end of the pipeline is rational, in the sense that it
computes a posterior distribution on student type conditional on all
information that it has available (college admissions, grades, and group
membership), and makes a decision based on posterior expectation. We then study
what kinds of fairness goals can be achieved by the college by setting its
admissions rule and grading policy. For example, the college might have the
goal of guaranteeing equal opportunity across populations: that the probability
of passing through the pipeline and being hired by the employer should be
independent of group membership, conditioned on type. Alternately, the college
might have the goal of incentivizing the employer to have a group blind hiring
rule. We show that both goals can be achieved when the college does not report
grades. On the other hand, we show that under reasonable conditions, these
goals are impossible to achieve even in isolation when the college uses an
(even minimally) informative grading policy.
COFGA: Classification Of Fine-Grained Features In Aerial Images
Authors: Eran Dahan, Tzvi Diskin
Date: 2018-08-27 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Scene Understanding

Classification between thousands of classes in high-resolution images is one
of the heavily studied problems in deep learning over the last decade. However,
the challenge of fine-grained multi-class classification of objects in aerial
images, especially in low resource cases, is still challenging and an active
area of research in the literature. Solving this problem can give rise to
various applications in the field of scene understanding and classification and
re-identification of specific objects from aerial images. In this paper, we
provide a description of our dataset - COFGA of multi-class annotated objects
in aerial images. We examine the results of existing state-of-the-art models
and modified deep neural networks. Finally, we explain in detail the first
published competition for solving this task.
Data Poisoning Attacks against Online Learning
Authors: Yizhen Wang, Kamalika Chaudhuri
Date: 2018-08-27 00:00:00
Tasks: Data Poisoning

We consider data poisoning attacks, a class of adversarial attacks on machine
learning where an adversary has the power to alter a small fraction of the
training data in order to make the trained classifier satisfy certain
objectives. While there has been much prior work on data poisoning, most of it
is in the offline setting, and attacks for online learning, where training data
arrives in a streaming manner, are not well understood.
  In this work, we initiate a systematic investigation of data poisoning
attacks for online learning. We formalize the problem into two settings, and we
propose a general attack strategy, formulated as an optimization problem, that
applies to both with some modifications. We propose three solution strategies,
and perform extensive experimental evaluation. Finally, we discuss the
implications of our findings for building successful defenses.
Open Set Chinese Character Recognition using Multi-typed Attributes
Authors: Sheng He, Lambert Schomaker
Date: 2018-08-27 00:00:00
Tasks: Attribute, Few-Shot Learning, General Classification, Image Retrieval, Retrieval, Zero-Shot Learning

Recognition of Off-line Chinese characters is still a challenging problem,
especially in historical documents, not only in the number of classes extremely
large in comparison to contemporary image retrieval methods, but also new
unseen classes can be expected under open learning conditions (even for CNN).
Chinese character recognition with zero or a few training samples is a
difficult problem and has not been studied yet. In this paper, we propose a new
Chinese character recognition method by multi-type attributes, which are based
on pronunciation, structure and radicals of Chinese characters, applied to
character recognition in historical books. This intermediate attribute code has
a strong advantage over the common `one-hot' class representation because it
allows for understanding complex and unseen patterns symbolically using
attributes. First, each character is represented by four groups of attribute
types to cover a wide range of character possibilities: Pinyin label, layout
structure, number of strokes, three different input methods such as Cangjie,
Zhengma and Wubi, as well as a four-corner encoding method. A convolutional
neural network (CNN) is trained to learn these attributes. Subsequently,
characters can be easily recognized by these attributes using a distance metric
and a complete lexicon that is encoded in attribute space. We evaluate the
proposed method on two open data sets: printed Chinese character recognition
for zero-shot learning, historical characters for few-shot learning and a
closed set: handwritten Chinese characters. Experimental results show a good
general classification of seen classes but also a very promising generalization
ability to unseen characters.
Large Margin Neural Language Model
Authors: Jiaji Huang, Yi Li, Wei Ping, Liang Huang
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, Machine Translation, model, speech-recognition, Speech Recognition, Translation

We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.
Models for Predicting Community-Specific Interest in News Articles
Authors: Benjamin D. Horne, William Dron, Sibel Adali
Date: 2018-08-27 00:00:00
Tasks: Articles

In this work, we ask two questions: 1. Can we predict the type of community
interested in a news article using only features from the article content? and
2. How well do these models generalize over time? To answer these questions, we
compute well-studied content-based features on over 60K news articles from 4
communities on reddit.com. We train and test models over three different time
periods between 2015 and 2017 to demonstrate which features degrade in
performance the most due to concept drift. Our models can classify news
articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0
ROC AUC. However, while we can predict the community-specific popularity of
news articles with high accuracy, practitioners should approach these models
carefully. Predictions are both community-pair dependent and feature group
dependent. Moreover, these feature groups generalize over time differently,
with some only degrading slightly over time, but others degrading greatly.
Therefore, we recommend that community-interest predictions are done in a
hierarchical structure, where multiple binary classifiers can be used to
separate community pairs, rather than a traditional multi-class model. Second,
these models should be retrained over time based on accuracy goals and the
availability of training data.
Behavior Trees as a Representation for Medical Procedures
Authors: Blake Hannaford, Randall Bly, Ian Humphreys, Mark Whipple
Date: 2018-08-27 00:00:00

Objective: Effective collaboration between machines and clinicians requires
flexible data structures to represent medical processes and clinical practice
guidelines. Such a data structure could enable effective turn-taking between
human and automated components of a complex treatment, accurate on-line
monitoring of clinical treatments (for example to detect medical errors), or
automated treatment systems (such as future medical robots) whose overall
treatment plan is understandable and auditable by human experts.
  Materials and Methods: Behavior trees (BTs) emerged from video game
development as a graphical language for modeling intelligent agent behavior.
BTs have several properties which are attractive for modeling medical
procedures including human-readability, authoring tools, and composability.
  Results: This paper will illustrate construction of BTs for exemplary medical
procedures and clinical protocols.
  Discussion and Conclusion: Behavior Trees thus form a useful, and human
authorable/readable bridge between clinical practice guidelines and AI systems.
Stereo 3D Object Trajectory Reconstruction
Authors: Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
Date: 2018-08-27 00:00:00
Tasks: Object, Optical Flow Estimation, Semantic Segmentation, Stereo Matching, Stereo Matching Hand

We present a method to reconstruct the three-dimensional trajectory of a
moving instance of a known object category using stereo video data. We track
the two-dimensional shape of objects on pixel level exploiting instance-aware
semantic segmentation techniques and optical flow cues. We apply Structure from
Motion (SfM) techniques to object and background images to determine for each
frame initial camera poses relative to object instances and background
structures. We refine the initial SfM results by integrating stereo camera
constraints exploiting factor graphs. We compute the object trajectory by
combining object and background camera pose information. In contrast to stereo
matching methods, our approach leverages temporal adjacent views for object
point triangulation. As opposed to monocular trajectory reconstruction
approaches, our method shows no degenerated cases. We evaluate our approach
using publicly available video data of vehicles in urban scenes.
Term Set Expansion based NLP Architect by Intel AI Lab
Authors: Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
Date: 2018-08-27 00:00:00
Proceeding: EMNLP 2018 11

We present SetExpander, a corpus-based system for expanding a seed set of
terms into amore complete set of terms that belong to the same semantic class.
SetExpander implements an iterative end-to-end workflow. It enables users to
easily select a seed set of terms, expand it, view the expanded set, validate
it, re-expand the validated set and store it, thus simplifying the extraction
of domain-specific fine-grained semantic classes.SetExpander has been used
successfully in real-life use cases including integration into an automated
recruitment system and an issues and defects resolution system. A video demo of
SetExpander is available at
https://drive.google.com/open?id=1e545bB87Autsch36DjnJHmq3HWfSd1Rv (some images
were blurred for privacy reasons)
Deep Learning of Vortex Induced Vibrations
Authors: Maziar Raissi, Zhicheng Wang, Michael S. Triantafyllou, George Em. Karniadakis
Date: 2018-08-26 00:00:00
Tasks: Deep Learning

Vortex induced vibrations of bluff bodies occur when the vortex shedding
frequency is close to the natural frequency of the structure. Of interest is
the prediction of the lift and drag forces on the structure given some limited
and scattered information on the velocity field. This is an inverse problem
that is not straightforward to solve using standard computational fluid
dynamics (CFD) methods, especially since no information is provided for the
pressure. An even greater challenge is to infer the lift and drag forces given
some dye or smoke visualizations of the flow field. Here we employ deep neural
networks that are extended to encode the incompressible Navier-Stokes equations
coupled with the structure's dynamic motion equation. In the first case, given
scattered data in space-time on the velocity field and the structure's motion,
we use four coupled deep neural networks to infer very accurately the
structural parameters, the entire time-dependent pressure field (with no prior
training data), and reconstruct the velocity vector field and the structure's
dynamic motion. In the second case, given scattered data in space-time on a
concentration field only, we use five coupled deep neural networks to infer
very accurately the vector velocity field and all other quantities of interest
as before. This new paradigm of inference in fluid mechanics for coupled
multi-physics problems enables velocity and pressure quantification from flow
snapshots in small subdomains and can be exploited for flow control
applications and also for system identification.