pwc-archive/papers-with-abstracts huggingface dataset:
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Authors: Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, Yan-ran Li
Date: 2016-04-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Query-focused Summarization, Saliency Ranking, Sentence

Query relevance ranking and sentence saliency ranking are the two main tasks
in extractive query-focused summarization. Previous supervised summarization
systems often perform the two tasks in isolation. However, since reference
summaries are the trade-off between relevance and saliency, using them as
supervision, neither of the two rankers could be trained well. This paper
proposes a novel summarization system called AttSum, which tackles the two
tasks jointly. It automatically learns distributed representations for
sentences as well as the document cluster. Meanwhile, it applies the attention
mechanism to simulate the attentive reading of human behavior when a query is
given. Extensive experiments are conducted on DUC query-focused summarization
benchmark datasets. Without using any hand-crafted features, AttSum achieves
competitive performance. It is also observed that the sentences recognized to
focus on the query indeed meet the query need.
Matrix Variate RBM Model with Gaussian Distributions
Authors: Simeng Liu, Yanfeng Sun, Yongli Hu, Junbin Gao, Bao-Cai Yin
Date: 2016-09-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, model

Restricted Boltzmann Machine (RBM) is a particular type of random neural
network models modeling vector data based on the assumption of Bernoulli
distribution. For multi-dimensional and non-binary data, it is necessary to
vectorize and discretize the information in order to apply the conventional
RBM. It is well-known that vectorization would destroy internal structure of
data, and the binary units will limit the applying performance due to fickle
real data. To address the issue, this paper proposes a Matrix variate Gaussian
Restricted Boltzmann Machine (MVGRBM) model for matrix data whose entries
follow Gaussian distributions. Compared with some other RBM algorithm, MVGRBM
can model real value data better and it has good performance in image
classification.
Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment
Authors: Tao Ge, Qing Dou, Xiaoman Pan, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Ming Zhou
Date: 2016-09-27 00:00:00
Tasks: Decipherment, Translation

Aligning coordinated text streams from multiple sources and multiple
languages has opened many new research venues on cross-lingual knowledge
discovery. In this paper we aim to advance state-of-the-art by: (1). extending
coarse-grained topic-level knowledge mining to fine-grained information units
such as entities and events; (2). following a novel
Data-to-Network-to-Knowledge (D2N2K) paradigm to construct and utilize network
structures to capture and propagate reliable evidence. We introduce a novel
Burst Information Network (BINet) representation that can display the most
important information and illustrate the connections among bursty entities,
events and keywords in the corpus. We propose an effective approach to
construct and decipher BINets, incorporating novel criteria based on
multi-dimensional clues from pronunciation, translation, burst, neighbor and
graph topological structure. The experimental results on Chinese and English
coordinated text streams show that our approach can accurately decipher the
nodes with high confidence in the BINets and that the algorithm can be
efficiently run in parallel, which makes it possible to apply it to huge
amounts of streaming data for never-ending language and information
decipherment.
Online Categorical Subspace Learning for Sketching Big Data with Misses
Authors: Yanning Shen, Morteza Mardani, Georgios B. Giannakis
Date: 2016-09-27 00:00:00
Tasks: Movie Recommendation, Quantization

With the scale of data growing every day, reducing the dimensionality (a.k.a.
sketching) of high-dimensional data has emerged as a task of paramount
importance. Relevant issues to address in this context include the sheer volume
of data that may consist of categorical samples, the typically streaming format
of acquisition, and the possibly missing entries. To cope with these
challenges, the present paper develops a novel categorical subspace learning
approach to unravel the latent structure for three prominent categorical
(bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit
and Tobit models treat data as quantized values of an analog-valued process
lying in a low-dimensional subspace, while the probabilistic Logit model relies
on low dimensionality of the data log-likelihood ratios. Leveraging the low
intrinsic dimensionality of the sought models, a rank regularized
maximum-likelihood estimator is devised, which is then solved recursively via
alternating majorization-minimization to sketch high-dimensional categorical
data `on the fly.' The resultant procedure alternates between sketching the new
incomplete datum and refining the latent subspace, leading to lightweight
first-order algorithms with highly parallelizable tasks per iteration. As an
extra degree of freedom, the quantization thresholds are also learned jointly
along with the subspace to enhance the predictive power of the sought models.
Performance of the subspace iterates is analyzed for both infinite and finite
data streams, where for the former asymptotic convergence to the stationary
point set of the batch estimator is established, while for the latter sublinear
regret bounds are derived for the empirical cost. Simulated tests with both
synthetic and real-world datasets corroborate the merits of the novel schemes
for real-time movie recommendation and chess-game classification.
BioLeaf: a professional mobile application to measure foliar damage caused by insect herbivory
Authors: Bruno Machado, Jonatan Orue, Mauro Arruda, Cleidimar Santos, Diogo Sarath, Wesley Goncalves, Gercina Silva, Hemerson Pistori, Antonia Roel, Jose Rodrigues-Jr
Date: 2016-09-26 00:00:00

Soybean is one of the ten greatest crops in the world, answering for
billion-dollar businesses every year. This crop suffers from insect herbivory
that costs millions from producers. Hence, constant monitoring of the crop
foliar damage is necessary to guide the application of insecticides. However,
current methods to measure foliar damage are expensive and dependent on
laboratory facilities, in some cases, depending on complex devices. To cope
with these shortcomings, we introduce an image processing methodology to
measure the foliar damage in soybean leaves. We developed a non-destructive
imaging method based on two techniques, Otsu segmentation and Bezier curves, to
estimate the foliar loss in leaves with or without border damage. We
instantiate our methodology in a mobile application named BioLeaf, which is
freely distributed for smartphone users. We experimented with real-world leaves
collected from a soybean crop in Brazil. Our results demonstrated that BioLeaf
achieves foliar damage quantification with precision comparable to that of
human specialists. With these results, our proposal might assist soybean
producers, reducing the time to measure foliar damage, reducing analytical
costs, and defining a commodity application that is applicable not only to soy,
but also to different crops such as cotton, bean, potato, coffee, and
vegetables.
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
Authors: Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Date: 2016-09-12 00:00:00

We consider the non-square matrix sensing problem, under restricted isometry
property (RIP) assumptions. We focus on the non-convex formulation, where any
rank-$r$ matrix $X \in \mathbb{R}^{m \times n}$ is represented as $UV^\top$,
where $U \in \mathbb{R}^{m \times r}$ and $V \in \mathbb{R}^{n \times r}$. In
this paper, we complement recent findings on the non-convex geometry of the
analogous PSD setting [5], and show that matrix factorization does not
introduce any spurious local minima, under RIP.
Discriminative Embeddings of Latent Variable Models for Structured Data
Authors: Hanjun Dai, Bo Dai, Le Song
Date: 2016-03-17 00:00:00
Tasks: Drug Design

Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations. We propose, structure2vec, an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Interestingly, structure2vec extracts features by performing a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. In applications involving millions of data points, we showed that structure2vec runs 2 times faster, produces models which are $10,000$ times smaller, while at the same time achieving the state-of-the-art predictive performance.
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Authors: William L. Hamilton, Jure Leskovec, Dan Jurafsky
Date: 2016-05-30 00:00:00
Proceeding: ACL 2016 8
Tasks: Diachronic Word Embeddings, Word Embeddings

Understanding how words change their meanings over time is key to models of
language and cultural evolution, but historical data on meaning is scarce,
making theories hard to develop and test. Word embeddings show promise as a
diachronic tool, but have not been carefully evaluated. We develop a robust
methodology for quantifying semantic change by evaluating word embeddings
(PPMI, SVD, word2vec) against known historical changes. We then use this
methodology to reveal statistical laws of semantic evolution. Using six
historical corpora spanning four languages and two centuries, we propose two
quantitative laws of semantic change: (i) the law of conformity---the rate of
semantic change scales with an inverse power-law of word frequency; (ii) the
law of innovation---independent of frequency, words that are more polysemous
have higher rates of semantic change.
Learning to Translate for Multilingual Question Answering
Authors: Ferhan Ture, Elizabeth Boschee
Date: 2016-09-26 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Question Answering, Translation

In multilingual question answering, either the question needs to be
translated into the document language, or vice versa. In addition to direction,
there are multiple methods to perform the translation, four of which we explore
in this paper: word-based, 10-best, context-based, and grammar-based. We build
a feature for each combination of translation direction and method, and train a
model that learns optimal feature weights. On a large forum dataset consisting
of posts in English, Arabic, and Chinese, our novel learn-to-translate approach
was more effective than a strong baseline (p<0.05): translating all text into
English, then training a classifier based only on English (original or
translated) text.
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection
Authors: Evgeny Burnaev, Ivan Koptelov, German Novikov, Timur Khanipov
Date: 2016-09-26 00:00:00
Tasks: Time Series, Time Series Analysis

Recurrent Neural Networks (RNNs) are extensively used for time-series
modeling and prediction. We propose an approach for automatic construction of a
binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for
detection of a vehicle passage through a checkpoint. As an input to the
classifier we use multidimensional signals of various sensors that are
installed on the checkpoint. Obtained results demonstrate that the previous
approach to handcrafting a classifier, consisting of a set of deterministic
rules, can be successfully replaced by an automatic RNN training on an
appropriately labelled data.
Variational Inference with Hamiltonian Monte Carlo
Authors: Christopher Wolf, Maximilian Karl, Patrick van der Smagt
Date: 2016-09-26 00:00:00
Tasks: Variational Inference

Variational inference lies at the core of many state-of-the-art algorithms.
To improve the approximation of the posterior beyond parametric families, it
was proposed to include MCMC steps into the variational lower bound. In this
work we explore this idea using steps of the Hamiltonian Monte Carlo (HMC)
algorithm, an efficient MCMC method. In particular, we incorporate the
acceptance step of the HMC algorithm, guaranteeing asymptotic convergence to
the true posterior. Additionally, we introduce some extensions to the HMC
algorithm geared towards faster convergence. The theoretical advantages of
these modifications are reflected by performance improvements in our
experimental results.
Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment
Authors: Shahriar Shariat, Vladimir Pavlovic
Date: 2016-09-26 00:00:00
Tasks: Computational Efficiency, EEG, Electroencephalogram (EEG), General Classification, Retrieval, Time Series, Time Series Analysis

Traditional pairwise sequence alignment is based on matching individual
samples from two sequences, under time monotonicity constraints. However, in
many application settings matching subsequences (segments) instead of
individual samples may bring in additional robustness to noise or local
non-causal perturbations. This paper presents an approach to segmental sequence
alignment that jointly segments and aligns two sequences, generalizing the
traditional per-sample alignment. To accomplish this task, we introduce a
distance metric between segments based on average pairwise distances and then
present a modified pair-HMM (PHMM) that incorporates the proposed distance
metric to solve the joint segmentation and alignment task. We also propose a
relaxation to our model that improves the computational efficiency of the
generic segmental PHMM. Our results demonstrate that this new measure of
sequence similarity can lead to improved classification performance, while
being resilient to noise, on a variety of sequence retrieval problems, from EEG
to motion sequence classification.
Online Segment to Segment Neural Transduction
Authors: Lei Yu, Jan Buys, Phil Blunsom
Date: 2016-09-26 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Morphological Inflection, Sentence, Sentence Summarization

We introduce an online neural sequence to sequence model that learns to
alternate between encoding and decoding segments of the input as it is read. By
independently tracking the encoding and decoding representations our algorithm
permits exact polynomial marginalization of the latent segmentation during
training, and during decoding beam search is employed to find the best
alignment path together with the predicted output sequence. Our model tackles
the bottleneck of vanilla encoder-decoders that have to read and memorize the
entire input sequence in their fixed-length hidden states before producing any
output. It is different from previous attentive models in that, instead of
treating the attention weights as output of a deterministic function, our model
assigns attention weights to a sequential latent variable which can be
marginalized out and permits online generation. Experiments on abstractive
sentence summarization and morphological inflection show significant
performance gains over the baseline encoder-decoders.
Stance Detection with Bidirectional Conditional Encoding
Authors: Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva
Date: 2016-06-17 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Stance Detection

Stance detection is the task of classifying the attitude expressed in a text
towards a target such as Hillary Clinton to be "positive", negative" or
"neutral". Previous work has assumed that either the target is mentioned in the
text or that training data for every target is given. This paper considers the
more challenging version of this task, where targets are not always mentioned
and no training data is available for the test targets. We experiment with
conditional LSTM encoding, which builds a representation of the tweet that is
dependent on the target, and demonstrate that it outperforms encoding the tweet
and the target independently. Performance is improved further when the
conditional model is augmented with bidirectional encoding. We evaluate our
approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving
performance second best only to a system trained on semi-automatically labelled
tweets for the test target. When such weak supervision is added, our approach
achieves state-of-the-art results.
An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages
Authors: Antonios Anastasopoulos, David Chiang, Long Duong
Date: 2016-09-26 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Clustering, Dynamic Time Warping, Translation

For many low-resource languages, spoken language resources are more likely to
be annotated with translations than with transcriptions. Translated speech data
is potentially valuable for documenting endangered languages or for training
speech translation systems. A first step towards making use of such data would
be to automatically align spoken words with their translations. We present a
model that combines Dyer et al.'s reparameterization of IBM Model 2
(fast-align) and k-means clustering using Dynamic Time Warping as a distance
metric. The two components are trained jointly using expectation-maximization.
In an extremely low-resource scenario, our model performs significantly better
than both a neural model and a strong baseline.
Learning Language-Visual Embedding for Movie Understanding with Natural-Language
Authors: Atousa Torabi, Niket Tandon, Leonid Sigal
Date: 2016-09-26 00:00:00
Tasks: Multiple-choice, Retrieval, Video Retrieval

Learning a joint language-visual embedding has a number of very appealing
properties and can result in variety of practical application, including
natural language image/video annotation and search. In this work, we study
three different joint language-visual neural network model architectures. We
evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)
Standard Ranking for video annotation and retrieval 2) Our proposed movie
multiple-choice test. This test facilitate automatic evaluation of
visual-language models for natural language video annotation based on human
activities. In addition to original Audio Description (AD) captions, provided
as part of LSMDC16, we collected and will make available a) manually generated
re-phrasings of those captions obtained using Amazon MTurk b) automatically
generated human activity elements in "Predicate + Object" (PO) phrases based on
"Knowlywood", an activity knowledge mining model. Our best model archives
Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset
of 1000 samples. For multiple-choice test, our best model achieve accuracy
58.11% over whole LSMDC16 public test-set.
Global Constraint Catalog, Volume II, Time-Series Constraints
Authors: Ekaterina Arafailova, Nicolas Beldiceanu, Rémi Douence, Mats Carlsson, Pierre Flener, María Andreína Francisco Rodríguez, Justin Pearson, Helmut Simonis
Date: 2016-09-26 00:00:00
Tasks: Time Series, Time Series Analysis

First this report presents a restricted set of finite transducers used to
synthesise structural time-series constraints described by means of a
multi-layered function composition scheme. Second it provides the corresponding
synthesised catalogue of structural time-series constraints where each
constraint is explicitly described in terms of automata with registers.
Randomized Social Choice Functions Under Metric Preferences
Authors: Elliot Anshelevich, John Postl
Date: 2015-12-23 00:00:00

We determine the quality of randomized social choice mechanisms in a setting
in which the agents have metric preferences: every agent has a cost for each
alternative, and these costs form a metric. We assume that these costs are
unknown to the mechanisms (and possibly even to the agents themselves), which
means we cannot simply select the optimal alternative, i.e. the alternative
that minimizes the total agent cost (or median agent cost). However, we do
assume that the agents know their ordinal preferences that are induced by the
metric space. We examine randomized social choice functions that require only
this ordinal information and select an alternative that is good in expectation
with respect to the costs from the metric. To quantify how good a randomized
social choice function is, we bound the distortion, which is the worst-case
ratio between expected cost of the alternative selected and the cost of the
optimal alternative. We provide new distortion bounds for a variety of
randomized mechanisms, for both general metrics and for important special
cases. Our results show a sizable improvement in distortion over deterministic
mechanisms.
Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs
Authors: Dionysios S. Kalogerias, Athina P. Petropulu
Date: 2016-02-16 00:00:00

In this work, we study stability of distributed filtering of Markov chains
with finite state space, partially observed in conditionally Gaussian noise. We
consider a nonlinear filtering scheme over a Distributed Network of Agents
(DNA), which relies on the distributed evaluation of the likelihood part of the
centralized nonlinear filter and is based on a particular specialization of the
Alternating Direction Method of Multipliers (ADMM) for fast average consensus.
Assuming the same number of consensus steps between any two consecutive noisy
measurements for each sensor in the network, we fully characterize a minimal
number of such steps, such that the distributed filter remains uniformly stable
with a prescribed accuracy level, {\varepsilon} \in (0,1], within a finite
operational horizon, T, and across all sensors. Stability is in the sense of
the \ell_1-norm between the centralized and distributed versions of the
posterior at each sensor, and at each time within T. Roughly speaking, our main
result shows that uniform {\varepsilon}-stability of the distributed filtering
process depends only loglinearly on T and (roughly) the size of the network,
and only logarithmically on 1/{\varepsilon}. If this total loglinear bound is
fulfilled, any additional consensus iterations will incur a fully quantified
further exponential decay in the consensus error. Our bounds are universal, in
the sense that they are independent of the particular structure of the Gaussian
Hidden Markov Model (HMM) under consideration.
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances
Authors: Frank Nielsen, Boris Muzellec, Richard Nock
Date: 2016-09-22 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

We consider the supervised classification problem of machine learning in
Cayley-Klein projective geometries: We show how to learn a curved Mahalanobis
metric distance corresponding to either the hyperbolic geometry or the elliptic
geometry using the Large Margin Nearest Neighbor (LMNN) framework. We report on
our experimental results, and further consider the case of learning a mixed
curved Mahalanobis distance. Besides, we show that the Cayley-Klein Voronoi
diagrams are affine, and can be built from an equivalent (clipped) power
diagrams, and that Cayley-Klein balls have Mahalanobis shapes with displaced
centers.