pwc-archive/papers-with-abstracts huggingface dataset:
DNN adaptation by automatic quality estimation of ASR hypotheses
Authors: Daniele Falavigna, Marco Matassoni, Shahab Jalalvand, Matteo Negri, Marco Turchi
Date: 2017-02-06 00:00:00
Tasks: Sentence

In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.
Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages
Authors: Iñaki San Vicente, Rodrigo Agerri, German Rigau
Date: 2017-02-06 00:00:00
Tasks: Sentiment Analysis

This paper presents a simple, robust and (almost) unsupervised
dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector)
to automatically generate polarity lexicons. We show that qwn-ppv outperforms
other automatically generated lexicons for the four extrinsic evaluations
presented here. It also shows very competitive and robust results with respect
to manually annotated ones. Results suggest that no single lexicon is best for
every task and dataset and that the intrinsic evaluation of polarity lexicons
is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv
method allows to easily create quality polarity lexicons whenever no
domain-based annotated corpora are available for a given language.
Distributed Evolutionary k-way Node Separators
Authors: Peter Sanders, Christian Schulz, Darren Strash, Robert Williger
Date: 2017-02-06 00:00:00

Computing high quality node separators in large graphs is necessary for a
variety of applications, ranging from divide-and-conquer algorithms to VLSI
design. In this work, we present a novel distributed evolutionary algorithm
tackling the k-way node separator problem. A key component of our contribution
includes new k-way local search algorithms based on maximum flows. We combine
our local search with a multilevel approach to compute an initial population
for our evolutionary algorithm, and further show how to modify the coarsening
stage of our multilevel algorithm to create effective combine and mutation
operations. Lastly, we combine these techniques with a scalable communication
protocol, producing a system that is able to compute high quality solutions in
a short amount of time. Our experiments against competing algorithms show that
our advanced evolutionary algorithm computes the best result on 94% of the
chosen benchmark instances.
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques
Authors: Marta R. Costa-jussà, Carlos Escolano
Date: 2016-10-07 00:00:00
Tasks: Classification, Deep Learning, Gender Classification, General Classification, Machine Translation, Translation

Morphology in unbalanced languages remains a big challenge in the context of
machine translation. In this paper, we propose to de-couple machine translation
from morphology generation in order to better deal with the problem. We
investigate the morphology simplification with a reasonable trade-off between
expected gain and generation complexity. For the Chinese-Spanish task, optimum
morphological simplification is in gender and number. For this purpose, we
design a new classification architecture which, compared to other standard
machine learning techniques, obtains the best results. This proposed
neural-based architecture consists of several layers: an embedding, a
convolutional followed by a recurrent neural network and, finally, ends with
sigmoid and softmax layers. We obtain classification results over 98% accuracy
in gender classification, over 93% in number classification, and an overall
translation improvement of 0.7 METEOR.
Coresets for Scalable Bayesian Logistic Regression
Authors: Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
Date: 2016-05-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Inference, regression, Uncertainty Quantification

The use of Bayesian methods in large-scale data settings is attractive
because of the rich hierarchical models, uncertainty quantification, and prior
specification they provide. Standard Bayesian inference algorithms are
computationally expensive, however, making their direct application to large
datasets difficult or infeasible. Recent work on scaling Bayesian inference has
focused on modifying the underlying algorithms to, for example, use only a
random data subsample at each iteration. We leverage the insight that data is
often redundant to instead obtain a weighted subset of the data (called a
coreset) that is much smaller than the original dataset. We can then use this
small coreset in any number of existing posterior inference algorithms without
modification. In this paper, we develop an efficient coreset construction
algorithm for Bayesian logistic regression models. We provide theoretical
guarantees on the size and approximation quality of the coreset -- both for
fixed, known datasets, and in expectation for a wide class of data generative
models. Crucially, the proposed approach also permits efficient construction of
the coreset in both streaming and parallel settings, with minimal additional
effort. We demonstrate the efficacy of our approach on a number of synthetic
and real-world datasets, and find that, in practice, the size of the coreset is
independent of the original dataset size. Furthermore, constructing the coreset
takes a negligible amount of time compared to that required to run MCMC on it.
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
Authors: Xinyu Li, Yanyi Zhang, Jianyu Zhang, Shuhong Chen, Ivan Marsic, Richard A. Farneth, Randall S. Burd
Date: 2017-02-06 00:00:00
Tasks: Activity Recognition, Concurrent Activity Recognition, Decision Making

We introduce a system that recognizes concurrent activities from real-world
data captured by multiple sensors of different types. The recognition is
achieved in two steps. First, we extract spatial and temporal features from the
multimodal data. We feed each datatype into a convolutional neural network that
extracts spatial features, followed by a long-short term memory network that
extracts temporal information in the sensory data. The extracted features are
then fused for decision making in the second step. Second, we achieve
concurrent activity recognition with a single classifier that encodes a binary
output vector in which elements indicate whether the corresponding activity
types are currently in progress. We tested our system with three datasets from
different domains recorded using different sensors and achieved performance
comparable to existing systems designed specifically for those domains. Our
system is the first to address the concurrent activity recognition with
multisensory data using a single model, which is scalable, simple to train and
easy to deploy.
Exploring the bidimensional space: A dynamic logic point of view
Authors: Philippe Balbiani, David Fernández-Duque, Emiliano Lorini
Date: 2017-02-06 00:00:00
Tasks: Position

We present a family of logics for reasoning about agents' positions and
motion in the plane which have several potential applications in the area of
multi-agent systems (MAS), such as multi-agent planning and robotics. The most
general logic includes (i) atomic formulas for representing the truth of a
given fact or the presence of a given agent at a certain position of the plane,
(ii) atomic programs corresponding to the four basic orientations in the plane
(up, down, left, right) as well as the four program constructs of propositional
dynamic logic (sequential composition, nondeterministic composition, iteration
and test). As this logic is not computably enumerable, we study some
interesting decidable and axiomatizable fragments of it. We also present a
decidable extension of the iteration-free fragment of the logic by special
programs representing motion of agents in the plane.
GuessWhat?! Visual object discovery through multi-modal dialogue
Authors: Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron Courville
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Discovery, Spatial Reasoning

We introduce GuessWhat?!, a two-player guessing game as a testbed for
research on the interplay of computer vision and dialogue systems. The goal of
the game is to locate an unknown object in a rich image scene by asking a
sequence of questions. Higher-level image understanding, like spatial reasoning
and language grounding, is required to solve the proposed task. Our key
contribution is the collection of a large-scale dataset consisting of 150K
human-played games with a total of 800K visual question-answer pairs on 66K
images. We explain our design decisions in collecting the dataset and introduce
the oracle and questioner tasks that are associated with the two players of the
game. We prototyped deep learning models to establish initial baselines of the
introduced tasks.
A Hybrid Approach For Hindi-English Machine Translation
Authors: Omkar Dhariya, Shrikant Malviya, Uma Shanker Tiwary
Date: 2017-02-06 00:00:00
Tasks: Machine Translation, Translation

In this paper, an extended combined approach of phrase based statistical
machine translation (SMT), example based MT (EBMT) and rule based MT (RBMT) is
proposed to develop a novel hybrid data driven MT system capable of
outperforming the baseline SMT, EBMT and RBMT systems from which it is derived.
In short, the proposed hybrid MT process is guided by the rule based MT after
getting a set of partial candidate translations provided by EBMT and SMT
subsystems. Previous works have shown that EBMT systems are capable of
outperforming the phrase-based SMT systems and RBMT approach has the strength
of generating structurally and morphologically more accurate results. This
hybrid approach increases the fluency, accuracy and grammatical precision which
improve the quality of a machine translation system. A comparison of the
proposed hybrid machine translation (HTM) model with renowned translators i.e.
Google, BING and Babylonian is also presented which shows that the proposed
model works better on sentences with ambiguity as well as comprised of idioms
than others.
Opinion Recommendation using Neural Memory Model
Authors: Zhongqing Wang, Yue Zhang
Date: 2017-02-06 00:00:00
Tasks: model

We present opinion recommendation, a novel task of jointly predicting a
custom review with a rating score that a certain user would give to a certain
product or service, given existing reviews and rating scores to the product or
service by other users, and the reviews that the user has given to other
products and services. A characteristic of opinion recommendation is the
reliance of multiple data sources for multi-task joint learning, which is the
strength of neural models. We use a single neural network to model users and
products, capturing their correlation and generating customised product
representations using a deep memory network, from which customised ratings and
reviews are constructed jointly. Results show that our opinion recommendation
system gives ratings that are closer to real user ratings on Yelp.com data
compared with Yelp's own ratings, and our methods give better results compared
to several pipelines baselines using state-of-the-art sentiment rating and
summarization systems.
Survey of modern Fault Diagnosis methods in networks
Authors: Zi Jian Yang, Yong Wang
Date: 2017-02-06 00:00:00
Tasks: Fault Diagnosis, Survey

With the advent of modern computer networks, fault diagnosis has been a focus
of research activity. This paper reviews the history of fault diagnosis in
networks and discusses the main methods in information gathering section,
information analyzing section and diagnosing and revolving section of fault
diagnosis in networks. Emphasis will be placed upon knowledge-based methods
with discussing the advantages and shortcomings of the different methods. The
survey is concluded with a description of some open problems.
Challenge of Multi-Camera Tracking
Authors: Yong Wang, Ke Lu
Date: 2017-02-06 00:00:00

Multi-camera tracking is quite different from single camera tracking, and it
faces new technology and system architecture challenges. By analyzing the
corresponding characteristics and disadvantages of the existing algorithms,
problems in multi-camera tracking are summarized and some new directions for
future work are also generalized.
Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification
Authors: Qiuyan Yan, Shixiong Xia, Fanrong Meng
Date: 2017-02-06 00:00:00
Tasks: General Classification

Class imbalance is one of the challenging problems for machine learning in
many real-world applications, such as coal and gas burst accident monitoring:
the burst premonition data is extreme smaller than the normal data, however,
which is the highlight we truly focus on. Cost-sensitive adjustment approach is
a typical algorithm-level method resisting the data set imbalance. For SVMs
classifier, which is modified to incorporate varying penalty parameter(C) for
each of considered groups of examples. However, the C value is determined
empirically, or is calculated according to the evaluation metric, which need to
be computed iteratively and time consuming. This paper presents a novel
cost-sensitive SVM method whose penalty parameter C optimized on the basis of
cluster probability density function(PDF) and the cluster PDF is estimated only
according to similarity matrix and some predefined hyper-parameters.
Experimental results on various standard benchmark data sets and real-world
data with different ratios of imbalance show that the proposed method is
effective in comparison with commonly used cost-sensitive techniques.
Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation
Authors: Kota Hara, Raviteja Vemulapalli, Rama Chellappa
Date: 2017-02-06 00:00:00

Deep Convolutional Neural Networks (DCNN) have been proven to be effective
for various computer vision problems. In this work, we demonstrate its
effectiveness on a continuous object orientation estimation task, which
requires prediction of 0 to 360 degrees orientation of the objects. We do so by
proposing and comparing three continuous orientation prediction approaches
designed for the DCNNs. The first two approaches work by representing an
orientation as a point on a unit circle and minimizing either L2 loss or
angular difference loss. The third method works by first converting the
continuous orientation estimation task into a set of discrete orientation
estimation tasks and then converting the discrete orientation outputs back to
the continuous orientation using a mean-shift algorithm. By evaluating on a
vehicle orientation estimation task and a pedestrian orientation estimation
task, we demonstrate that the discretization-based approach not only works
better than the other two approaches but also achieves state-of-the-art
performance. We also demonstrate that finding an appropriate feature
representation is critical to achieve a good performance when adapting a DCNN
trained for an image recognition task.
Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM
Authors: Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang
Date: 2016-05-17 00:00:00

Modeling human conversations is the essence for building satisfying chat-bots
with multi-turn dialog ability. Conversation modeling will notably benefit from
domain knowledge since the relationships between sentences can be clarified due
to semantic hints introduced by knowledge. In this paper, a deep neural network
is proposed to incorporate background knowledge for conversation modeling.
Through a specially designed Recall gate, domain knowledge can be transformed
into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance
LSTM by cooperating with its local memory to capture the implicit semantic
relevance between sentences within conversations. In addition, this paper
introduces the loose structured domain knowledge base, which can be built with
slight amount of manual work and easily adopted by the Recall gate. Our model
is evaluated on the context-oriented response selecting task, and experimental
results on both two datasets have shown that our approach is promising for
modeling human conversations and building key components of automatic chatting
systems.
KU-ISPL Speaker Recognition Systems under Language mismatch condition for NIST 2016 Speaker Recognition Evaluation
Authors: Suwon Shon, Hanseok Ko
Date: 2017-02-03 00:00:00
Tasks: Clustering, Speaker Recognition

Korea University Intelligent Signal Processing Lab. (KU-ISPL) developed
speaker recognition system for SRE16 fixed training condition. Data for
evaluation trials are collected from outside North America, spoken in Tagalog
and Cantonese while training data only is spoken English. Thus, main issue for
SRE16 is compensating the discrepancy between different languages. As
development dataset which is spoken in Cebuano and Mandarin, we could prepare
the evaluation trials through preliminary experiments to compensate the
language mismatched condition. Our team developed 4 different approaches to
extract i-vectors and applied state-of-the-art techniques as backend. To
compensate language mismatch, we investigated and endeavored unique method such
as unsupervised language clustering, inter language variability compensation
and gender/language dependent score normalization.
Feature Selection for Classification under Anonymity Constraint
Authors: Baichuan Zhang, Noman Mohammed, Vachik Dave, Mohammad Al Hasan
Date: 2015-12-22 00:00:00
Tasks: Classification, Diversity, feature selection, General Classification

Over the last decade, proliferation of various online platforms and their
increasing adoption by billions of users have heightened the privacy risk of a
user enormously. In fact, security researchers have shown that sparse microdata
containing information about online activities of a user although anonymous,
can still be used to disclose the identity of the user by cross-referencing the
data with other data sources. To preserve the privacy of a user, in existing
works several methods (k-anonymity, l-diversity, differential privacy) are
proposed that ensure a dataset which is meant to share or publish bears small
identity disclosure risk. However, the majority of these methods modify the
data in isolation, without considering their utility in subsequent knowledge
discovery tasks, which makes these datasets less informative. In this work, we
consider labeled data that are generally used for classification, and propose
two methods for feature selection considering two goals: first, on the reduced
feature set the data has small disclosure risk, and second, the utility of the
data is preserved for performing a classification task. Experimental results on
various real-world datasets show that the method is effective and useful in
practice.
Learning about Spanish dialects through Twitter
Authors: Bruno Gonçalves, David Sánchez
Date: 2015-11-16 00:00:00
Tasks: BIG-bench Machine Learning

This paper maps the large-scale variation of the Spanish language by
employing a corpus based on geographically tagged Twitter messages. Lexical
dialects are extracted from an analysis of variants of tens of concepts. The
resulting maps show linguistic variation on an unprecedented scale across the
globe. We discuss the properties of the main dialects within a machine learning
approach and find that varieties spoken in urban areas have an international
character in contrast to country areas where dialects show a more regional
uniformity.
Attentional Network for Visual Object Detection
Authors: Kota Hara, Ming-Yu Liu, Oncel Tuzel, Amir-Massoud Farahmand
Date: 2017-02-06 00:00:00
Tasks: Object, object-detection, Object Detection, Reinforcement Learning

We propose augmenting deep neural networks with an attention mechanism for
the visual object detection task. As perceiving a scene, humans have the
capability of multiple fixation points, each attended to scene content at
different locations and scales. However, such a mechanism is missing in the
current state-of-the-art visual object detection methods. Inspired by the human
vision system, we propose a novel deep network architecture that imitates this
attention mechanism. As detecting objects in an image, the network adaptively
places a sequence of glimpses of different shapes at different locations in the
image. Evidences of the presence of an object and its location are extracted
from these glimpses, which are then fused for estimating the object class and
bounding box coordinates. Due to lacks of ground truth annotations of the
visual attention mechanism, we train our network using a reinforcement learning
algorithm with policy gradients. Experiment results on standard object
detection benchmarks show that the proposed network consistently outperforms
the baseline networks that does not model the attention mechanism.
Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields
Authors: Yi Zhou, Laurent Kneip, Hongdong Li
Date: 2017-02-06 00:00:00
Tasks: Visual Odometry

This paper presents a robust and efficient semi-dense visual odometry
solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline
which estimates the pose of the sensor by registering the projection of a 3D
semi-dense map of the reference frame with the 2D semi-dense region extracted
in the current frame. The processing is speeded up by efficiently implemented
approximate nearest neighbour fields under the Euclidean distance criterion,
which permits the use of compact Gauss-Newton updates in the optimization. The
registration is formulated as a maximum a posterior problem to deal with
outliers and sensor noises, and consequently the equivalent weighted least
squares problem is solved by iteratively reweighted least squares method. A
variety of robust weight functions are tested and the optimum is determined
based on the characteristics of the sensor model. Extensive evaluation on
publicly available RGB-D datasets shows that the proposed method predominantly
outperforms existing state-of-the-art methods.
Prepositions in Context
Authors: Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath
Date: 2017-02-05 00:00:00
Tasks: Clustering

Prepositions are highly polysemous, and their variegated senses encode
significant semantic information. In this paper we match each preposition's
complement and attachment and their interplay crucially to the geometry of the
word vectors to the left and right of the preposition. Extracting such features
from the vast number of instances of each preposition and clustering them makes
for an efficient preposition sense disambigution (PSD) algorithm, which is
comparable to and better than state-of-the-art on two benchmark datasets. Our
reliance on no external linguistic resource allows us to scale the PSD
algorithm to a large WikiCorpus and learn sense-specific preposition
representations -- which we show to encode semantic relations and paraphrasing
of verb particle compounds, via simple vector operations.