pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Risk Factor Domains in Psychosis Patient Health Records
Authors: Eben Holderness, Nicholas Miller, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Marie Meteer, Mei-Hua Hall
Date: 2018-09-15 00:00:00
Proceeding: WS 2018 10
Tasks: Readmission Prediction

Readmission after discharge from a hospital is disruptive and costly,
regardless of the reason. However, it can be particularly problematic for
psychiatric patients, so predicting which patients may be readmitted is
critically important but also very difficult. Clinical narratives in
psychiatric electronic health records (EHRs) span a wide range of topics and
vocabulary; therefore, a psychiatric readmission prediction model must begin
with a robust and interpretable topic extraction component. We created a data
pipeline for using document vector similarity metrics to perform topic
extraction on psychiatric EHR data in service of our long-term goal of creating
a readmission risk classifier. We show initial results for our topic extraction
model and identify additional features we will be incorporating in the future.
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared Task
Authors: Fynn Schröder, Marcel Kamlot, Gregor Billing, Arne Köhn
Date: 2018-09-15 00:00:00
Tasks: Morphological Inflection

In this paper we describe the system submitted by UHH to the
CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection. We
propose a neural architecture based on the concepts of UZH (Makarov et al.,
2017), adding new ideas and techniques to their key concept and evaluating
different combinations of parameters. The resulting system is a
language-agnostic network model that aims to reduce the number of learned edit
operations by introducing equivalence classes over graphical features of
individual characters. We try to pinpoint advantages and drawbacks of this
approach by comparing different network configurations and evaluating our
results over a wide range of languages.
Neural Networks and Quantifier Conservativity: Does Data Distribution Affect Learnability?
Authors: Vishwali Mhasawade, Ildikó Emese Szabó, Melanie Tosik, Sheng-Fu Wang
Date: 2018-09-15 00:00:00
Tasks: Language Acquisition

All known natural language determiners are conservative. Psycholinguistic
experiments indicate that children exhibit a corresponding learnability bias
when faced with the task of learning new determiners. However, recent work
indicates that this bias towards conservativity is not observed during the
training stage of artificial neural networks. In this work, we investigate
whether the learnability bias exhibited by children is in part due to the
distribution of quantifiers in natural language. We share results of five
experiments, contrasted by the distribution of conservative vs.
non-conservative determiners in the training data. We demonstrate that the
aquisitional issues with non-conservative quantifiers can not be explained by
the distribution of natural language data, which favors conservative
quantifiers. This finding indicates that the bias in language acquisition data
might be innate or representational.
Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Authors: Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Date: 2018-09-15 00:00:00
Tasks: ARC, Information Retrieval, Multiple-choice, Natural Language Inference, Open-Domain Question Answering, Question Answering, Retrieval

Open-domain question answering (QA) is an important problem in AI and NLP
that is emerging as a bellwether for progress on the generalizability of AI
methods and techniques. Much of the progress in open-domain QA systems has been
realized through advances in information retrieval methods and corpus
construction. In this paper, we focus on the recently introduced ARC Challenge
dataset, which contains 2,590 multiple choice questions authored for
grade-school science exams. These questions are selected to be the most
challenging for current QA systems, and current state of the art performance is
only slightly better than random chance. We present a system that rewrites a
given question into queries that are used to retrieve supporting text from a
large corpus of science-related text. Our rewriter is able to incorporate
background knowledge from ConceptNet and -- in tandem with a generic textual
entailment system trained on SciTail that identifies support in the retrieved
results -- outperforms several strong baselines on the end-to-end QA task
despite only being trained to identify essential terms in the original source
question. We use a generalizable decision methodology over the retrieved
evidence and answer candidates to select the best answer. By combining query
rewriting, background knowledge, and textual entailment our system is able to
outperform several strong baselines on the ARC dataset.
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
Authors: Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
Date: 2018-09-15 00:00:00
Tasks: Knowledge Graphs, Natural Language Inference, Question Answering

Natural Language Inference (NLI) is fundamental to many Natural Language
Processing (NLP) applications including semantic search and question answering.
The NLI problem has gained significant attention thanks to the release of large
scale, challenging datasets. Present approaches to the problem largely focus on
learning-based methods that use only textual information in order to classify
whether a given premise entails, contradicts, or is neutral with respect to a
given hypothesis. Surprisingly, the use of methods based on structured
knowledge -- a central topic in artificial intelligence -- has not received
much attention vis-a-vis the NLI problem. While there are many open knowledge
bases that contain various types of reasoning information, their use for NLI
has not been well explored. To address this, we present a combination of
techniques that harness knowledge graphs to improve performance on the NLI
problem in the science questions domain. We present the results of applying our
techniques on text, graph, and text-to-graph based models, and discuss
implications for the use of external knowledge in solving the NLI problem. Our
model achieves the new state-of-the-art performance on the NLI problem over the
SciTail science questions dataset.
Incorporating Behavioral Constraints in Online AI Systems
Authors: Avinash Balakrishnan, Djallel Bouneffouf, Nicholas Mattei, Francesca Rossi
Date: 2018-09-15 00:00:00
Tasks: Thompson Sampling

AI systems that learn through reward feedback about the actions they take are
increasingly deployed in domains that have significant impact on our daily
life. However, in many cases the online rewards should not be the only guiding
criteria, as there are additional constraints and/or priorities imposed by
regulations, values, preferences, or ethical principles. We detail a novel
online agent that learns a set of behavioral constraints by observation and
uses these learned constraints as a guide when making decisions in an online
setting while still being reactive to reward feedback. To define this agent, we
propose to adopt a novel extension to the classical contextual multi-armed
bandit setting and we provide a new algorithm called Behavior Constrained
Thompson Sampling (BCTS) that allows for online learning while obeying
exogenous constraints. Our agent learns a constrained policy that implements
the observed behavioral constraints demonstrated by a teacher agent, and then
uses this constrained policy to guide the reward-based online exploration and
exploitation. We characterize the upper bound on the expected regret of the
contextual bandit algorithm that underlies our agent and provide a case study
with real world data in two application domains. Our experiments show that the
designed agent is able to act within the set of behavior constraints without
significantly degrading its overall reward performance.
Multi-Scale Deep Compressive Sensing Network
Authors: Thuong Nguyen Canh, Byeungwoo Jeon
Date: 2018-09-15 00:00:00
Tasks: Compressive Sensing

With joint learning of sampling and recovery, the deep learning-based
compressive sensing (DCS) has shown significant improvement in performance and
running time reduction. Its reconstructed image, however, losses high-frequency
content especially at low subrates. This happens similarly in the multi-scale
sampling scheme which also samples more low-frequency components. In this
paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in
which we convert image signal using multiple scale-based wavelet transform,
then capture it through convolution block by block across scales. The initial
reconstructed image is directly recovered from multi-scale measurements.
Multi-scale wavelet convolution is utilized to enhance the final reconstruction
quality. The network is able to learn both multi-scale sampling and multi-scale
reconstruction, thus results in better reconstruction quality.
Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts
Authors: Chih-Wen Goo, Yun-Nung Chen
Date: 2018-09-15 00:00:00
Tasks: Abstractive Dialogue Summarization, Abstractive Text Summarization, Sentence

Neural abstractive summarization has been increasingly studied, where the
prior work mainly focused on summarizing single-speaker documents (news,
scientific publications, etc). In dialogues, there are different interactions
between speakers, which are usually defined as dialogue acts. The interactive
signals may provide informative cues for better summarizing dialogues. This
paper proposes to explicitly leverage dialogue acts in a neural summarization
model, where a sentence-gated mechanism is designed for modeling the
relationship between dialogue acts and the summary. The experiments show that
our proposed model significantly improves the abstractive summarization
performance compared to the state-of-the-art baselines on AMI meeting corpus,
demonstrating the usefulness of the interactive signal provided by dialogue
acts.
Learning Robust Manipulation Skills with Guided Policy Search via Generative Motor Reflexes
Authors: Philipp Ennen, Pia Bresenitz, Rene Vossen, Frank Hees
Date: 2018-09-15 00:00:00

Guided Policy Search enables robots to learn control policies for complex
manipulation tasks efficiently. Therein, the control policies are represented
as high-dimensional neural networks which derive robot actions based on states.
However, due to the small number of real-world trajectory samples in Guided
Policy Search, the resulting neural networks are only robust in the
neighbourhood of the trajectory distribution explored by real-world
interactions. In this paper, we present a new policy representation called
Generative Motor Reflexes, which is able to generate robust actions over a
broader state space compared to previous methods. In contrast to prior
state-action policies, Generative Motor Reflexes map states to parameters for a
state-dependent motor reflex, which is then used to derive actions. Robustness
is achieved by generating similar motor reflexes for many states. We evaluate
the presented method in simulated and real-world manipulation tasks, including
contact-rich peg-in-hole tasks. Using these evaluation tasks, we show that
policies represented as Generative Motor Reflexes lead to robust manipulation
skills also outside the explored trajectory distribution with less training
needs compared to previous methods.
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
Authors: Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama
Date: 2018-09-15 00:00:00

We consider a problem of learning a binary classifier only from positive data
and unlabeled data (PU learning) and estimating the class-prior in unlabeled
data under the case-control scenario. Most of the recent methods of PU learning
require an estimate of the class-prior probability in unlabeled data, and it is
estimated in advance with another method. However, such a two-step approach
which first estimates the class prior and then trains a classifier may not be
the optimal approach since the estimation error of the class-prior is not taken
into account when a classifier is trained. In this paper, we propose a novel
unified approach to estimating the class-prior and training a classifier
alternately. Our proposed method is simple to implement and computationally
efficient. Through experiments, we demonstrate the practical usefulness of the
proposed method.
Inferring Political Alignments of Twitter Users: A case study on 2017 Turkish constitutional referendum
Authors: Kutlu Emre Yilmaz, Osman Abul
Date: 2018-09-15 00:00:00

Increasing popularity of Twitter in politics is subject to commercial and
academic interest. To fully exploit the merits of this platform, reaching the
target audience with desired political leanings is critical. This paper extends
the research on inferring political orientations of Twitter users to the case
of 2017 Turkish constitutional referendum. After constructing a targeted
dataset of tweets, we explore several types of potential features to build
accurate machine learning based predictive models. In our experiments, a
three-class support vector machine (SVM) classifier trained on semantic
features achieves the best accuracy score of 89.9%. Moreover, an SVM classifier
trained on full-text features performs better than an SVM classifier trained on
hashtags, with respective accuracy scores of 89.05% and 85.9%. Relatively high
accuracy scores obtained by full-text features may point to differences in
language use, which deserves further research.
CLUSE: Cross-Lingual Unsupervised Sense Embeddings
Authors: Ta-Chung Chi, Yun-Nung Chen
Date: 2018-09-15 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Representation Learning, Word Similarity

This paper proposes a modularized sense induction and representation learning
model that jointly learns bilingual sense embeddings that align well in the
vector space, where the cross-lingual signal in the English-Chinese parallel
corpus is exploited to capture the collocation and distributed characteristics
in the language pair. The model is evaluated on the Stanford Contextual Word
Similarity (SCWS) dataset to ensure the quality of monolingual sense
embeddings. In addition, we introduce Bilingual Contextual Word Similarity
(BCWS), a large and high-quality dataset for evaluating cross-lingual sense
embeddings, which is the first attempt of measuring whether the learned
embeddings are indeed aligned well in the vector space. The proposed approach
shows the superior quality of sense embeddings evaluated in both monolingual
and bilingual spaces.
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Authors: Annamalai Narayanan, Charlie Soh, Lihui Chen, Yang Liu, Lipo Wang
Date: 2018-09-15 00:00:00
Tasks: Clone Detection, Clustering, Malware Analysis, Malware Detection, Representation Learning

Building behavior profiles of Android applications (apps) with holistic, rich
and multi-view information (e.g., incorporating several semantic views of an
app such as API sequences, system calls, etc.) would help catering downstream
analytics tasks such as app categorization, recommendation and malware analysis
significantly better. Towards this goal, we design a semi-supervised
Representation Learning (RL) framework named apk2vec to automatically generate
a compact representation (aka profile/embedding) for a given app. More
specifically, apk2vec has the three following unique characteristics which make
it an excellent choice for largescale app profiling: (1) it encompasses
information from multiple semantic views such as API sequences, permissions,
etc., (2) being a semi-supervised embedding technique, it can make use of
labels associated with apps (e.g., malware family or app category labels) to
build high quality app profiles, and (3) it combines RL and feature hashing
which allows it to efficiently build profiles of apps that stream over time
(i.e., online learning). The resulting semi-supervised multi-view hash
embeddings of apps could then be used for a wide variety of downstream tasks
such as the ones mentioned above. Our extensive evaluations with more than
42,000 apps demonstrate that apk2vec's app profiles could significantly
outperform state-of-the-art techniques in four app analytics tasks namely,
malware detection, familial clustering, app clone detection and app
recommendation.
Attention as a Perspective for Learning Tempo-invariant Audio Queries
Authors: Matthias Dorfer, Jan Hajič jr., Gerhard Widmer
Date: 2018-09-15 00:00:00
Tasks: Retrieval

Current models for audio--sheet music retrieval via multimodal embedding
space learning use convolutional neural networks with a fixed-size window for
the input audio. Depending on the tempo of a query performance, this window
captures more or less musical content, while notehead density in the score is
largely tempo-independent. In this work we address this disparity with a soft
attention mechanism, which allows the model to encode only those parts of an
audio excerpt that are most relevant with respect to efficient query codes.
Empirical results on classical piano music indicate that attention is
beneficial for retrieval performance, and exhibits intuitively appealing
behavior.
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Authors: Wenhao Ding, Wenshuo Wang, Ding Zhao
Date: 2018-09-15 00:00:00
Tasks: Autonomous Vehicles, Decoder, Disentanglement

Generating multi-vehicle trajectories from existing limited data can provide
rich resources for autonomous vehicle development and testing. This paper
introduces a multi-vehicle trajectory generator (MTG) that can encode
multi-vehicle interaction scenarios (called driving encounters) into an
interpretable representation from which new driving encounter scenarios are
generated by sampling. The MTG consists of a bi-directional encoder and a
multi-branch decoder. A new disentanglement metric is then developed for model
analyses and comparisons in terms of model robustness and the independence of
the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN
demonstrates that the MTG has stronger capability to purposely generate
rational vehicle-to-vehicle encounters through operating the disentangled
latent codes. Thus the MTG could provide more data for engineers and
researchers to develop testing and evaluation scenarios for autonomous
vehicles.
Graph Convolutional Networks for Text Classification
Authors: Liang Yao, Chengsheng Mao, Yuan Luo
Date: 2018-09-15 00:00:00
Tasks: General Classification, Sentiment Analysis, Text Classification

Text classification is an important and classical problem in natural language
processing. There have been a number of studies that applied convolutional
neural networks (convolution on regular grid, e.g., sequence) to
classification. However, only a limited number of studies have explored the
more flexible graph convolutional neural networks (convolution on non-grid,
e.g., arbitrary graph) for the task. In this work, we propose to use graph
convolutional networks for text classification. We build a single text graph
for a corpus based on word co-occurrence and document word relations, then
learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text
GCN is initialized with one-hot representation for word and document, it then
jointly learns the embeddings for both words and documents, as supervised by
the known class labels for documents. Our experimental results on multiple
benchmark datasets demonstrate that a vanilla Text GCN without any external
word embeddings or knowledge outperforms state-of-the-art methods for text
classification. On the other hand, Text GCN also learns predictive word and
document embeddings. In addition, experimental results show that the
improvement of Text GCN over state-of-the-art comparison methods become more
prominent as we lower the percentage of training data, suggesting the
robustness of Text GCN to less training data in text classification.
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
Authors: Prabhat Nagarajan, Garrett Warnell, Peter Stone
Date: 2018-09-15 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training. To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance. We find that individual sources of nondeterminism can substantially impact the performance of agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.
Wasserstein Autoencoders for Collaborative Filtering
Authors: Jingbin Zhong, Xiaofeng Zhang
Date: 2018-09-15 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

The recommender systems have long been investigated in the literature.
Recently, users' implicit feedback like `click' or `browse' are considered to
be able to enhance the recommendation performance. Therefore, a number of
attempts have been made to resolve this issue. Among them, the variational
autoencoders (VAE) approach already achieves a superior performance. However,
the distributions of the encoded latent variables overlap a lot which may
restrict its recommendation ability. To cope with this challenge, this paper
tries to extend the Wasserstein autoencoders (WAE) for collaborative filtering.
Particularly, the loss function of the adapted WAE is re-designed by
introducing two additional loss terms: (1) the mutual information loss between
the distribution of latent variables and the assumed ground truth distribution,
and (2) the L1 regularization loss introduced to restrict the encoded latent
variables to be sparse. Two different cost functions are designed for measuring
the distance between the implicit feedback data and its re-generated version of
data. Experiments are valuated on three widely adopted data sets, i.e., ML-20M,
Netflix and LASTFM. Both the baseline and the state-of-the-art approaches are
chosen for the performance comparison which are Mult-DAE, Mult-VAE, CDAE and
Slim. The performance of the proposed approach outperforms the compared methods
with respect to evaluation criteria Recall@1, Recall@5 and NDCG@10, and this
demonstrates the efficacy of the proposed approach.