pwc-archive/papers-with-abstracts huggingface dataset:
A Minimal Span-Based Neural Constituency Parser
Authors: Mitchell Stern, Jacob Andreas, Dan Klein
Date: 2017-05-10 00:00:00
Proceeding: ACL 2017 7
Tasks: Constituency Parsing

In this work, we present a minimal neural model for constituency parsing
based on independent scoring of labels and spans. We show that this model is
not only compatible with classical dynamic programming techniques, but also
admits a novel greedy top-down inference algorithm based on recursive
partitioning of the input. We demonstrate empirically that both prediction
schemes are competitive with recent work, and when combined with basic
extensions to the scoring model are capable of achieving state-of-the-art
single-model performance on the Penn Treebank (91.79 F1) and strong performance
on the French Treebank (82.23 F1).
Solving Distributed Constraint Optimization Problems Using Logic Programming
Authors: Tiep Le, Tran Cao Son, Enrico Pontelli, William Yeoh
Date: 2017-05-10 00:00:00

This paper explores the use of Answer Set Programming (ASP) in solving
Distributed Constraint Optimization Problems (DCOPs). The paper provides the
following novel contributions: (1) It shows how one can formulate DCOPs as
logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is
based on logic programming; (3) It experimentally shows that ASP-DPOP can be up
to two orders of magnitude faster than DPOP (its imperative programming
counterpart) as well as solve some problems that DPOP fails to solve, due to
memory limitations; and (4) It demonstrates the applicability of ASP in a wide
array of multi-agent problems currently modeled as DCOPs. Under consideration
in Theory and Practice of Logic Programming (TPLP).
Tracking the World State with Recurrent Entity Networks
Authors: Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann Lecun
Date: 2016-12-12 00:00:00
Tasks: Procedural Text Understanding, Question Answering

We introduce a new model, the Recurrent Entity Network (EntNet). It is
equipped with a dynamic long-term memory which allows it to maintain and update
a representation of the state of the world as it receives new data. For
language understanding tasks, it can reason on-the-fly as it reads text, not
just when it is required to answer a question or respond as is the case for a
Memory Network (Sukhbaatar et al., 2015). Like a Neural Turing Machine or
Differentiable Neural Computer (Graves et al., 2014; 2016) it maintains a fixed
size memory and can learn to perform location and content-based read and write
operations. However, unlike those models it has a simple parallel architecture
in which several memory locations can be updated simultaneously. The EntNet
sets a new state-of-the-art on the bAbI tasks, and is the first method to solve
all the tasks in the 10k training examples setting. We also demonstrate that it
can solve a reasoning task which requires a large number of supporting facts,
which other methods are not able to solve, and can generalize past its training
horizon. It can also be practically used on large scale datasets such as
Children's Book Test, where it obtains competitive performance, reading the
story in a single pass.
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
Authors: Sabrina Klos, Cem Tekin, Mihaela van der Schaar, Anja Klein
Date: 2017-05-10 00:00:00

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human
intelligence tasks. MCS requires an appropriate task assignment strategy, since
different workers may have different performance in terms of acceptance rate
and quality. Task assignment is challenging, since a worker's performance (i)
may fluctuate, depending on both the worker's current personal context and the
task context, (ii) is not known a priori, but has to be learned over time.
Moreover, learning context-specific worker performance requires access to
context information, which may not be available at a central entity due to
communication overhead or privacy concerns. Additionally, evaluating worker
performance might require costly quality assessments. In this paper, we propose
a context-aware hierarchical online learning algorithm addressing the problem
of performance maximization in MCS. In our algorithm, a local controller (LC)
in the mobile device of a worker regularly observes the worker's context,
her/his decisions to accept or decline tasks and the quality in completing
tasks. Based on these observations, the LC regularly estimates the worker's
context-specific performance. The mobile crowdsourcing platform (MCSP) then
selects workers based on performance estimates received from the LCs. This
hierarchical approach enables the LCs to learn context-specific worker
performance and it enables the MCSP to select suitable workers. In addition,
our algorithm preserves worker context locally, and it keeps the number of
required quality assessments low. We prove that our algorithm converges to the
optimal task assignment strategy. Moreover, the algorithm outperforms simpler
task assignment strategies in experiments based on synthetic and real data.
A Survey of Question Answering for Math and Science Problem
Authors: Arindam Bhattacharya
Date: 2017-05-10 00:00:00
Tasks: Math, Question Answering, Survey

Turing test was long considered the measure for artificial intelligence. But
with the advances in AI, it has proved to be insufficient measure. We can now
aim to mea- sure machine intelligence like we measure human intelligence. One
of the widely accepted measure of intelligence is standardized math and science
test. In this paper, we explore the progress we have made towards the goal of
making a machine smart enough to pass the standardized test. We see the
challenges and opportunities posed by the domain, and note that we are quite
some ways from actually making a system as smart as a even a middle school
scholar.
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Authors: Ivan Vulić, Daniela Gerz, Douwe Kiela, Felix Hill, Anna Korhonen
Date: 2016-08-06 00:00:00
Proceeding: CL 2017 12
Tasks: Lexical Entailment, Relation, Representation Learning

We introduce HyperLex - a dataset and evaluation resource that quantifies the
extent of of the semantic category membership, that is, type-of relation also
known as hyponymy-hypernymy or lexical entailment (LE) relation between 2,616
concept pairs. Cognitive psychology research has established that typicality
and category/class membership are computed in human semantic memory as a
gradual rather than binary relation. Nevertheless, most NLP research, and
existing large-scale invetories of concept category membership (WordNet,
DBPedia, etc.) treat category membership and LE as binary. To address this, we
asked hundreds of native English speakers to indicate typicality and strength
of category membership between a diverse range of concept pairs on a
crowdsourcing platform. Our results confirm that category membership and LE are
indeed more gradual than binary. We then compare these human judgements with
the predictions of automatic systems, which reveals a huge gap between human
performance and state-of-the-art LE, distributional and representation learning
models, and substantial differences between the models themselves. We discuss a
pathway for improving semantic models to overcome this discrepancy, and
indicate future application areas for improved graded LE systems.
Analysing Data-To-Text Generation Benchmarks
Authors: Laura Perez-Beltrachini, Claire Gardent
Date: 2017-05-10 00:00:00
Proceeding: WS 2017 9
Tasks: Data-to-Text Generation, Text Generation

Recently, several data-sets associating data to text have been created to
train data-to-text surface realisers. It is unclear however to what extent the
surface realisation task exercised by these data-sets is linguistically
challenging. Do these data-sets provide enough variety to encourage the
development of generic, high-quality data-to-text surface realisers ? In this
paper, we argue that these data-sets have important drawbacks. We back up our
claim using statistics, metrics and manual evaluation. We conclude by eliciting
a set of criteria for the creation of a data-to-text benchmark which could help
better support the development, evaluation and comparison of linguistically
sophisticated data-to-text surface realisers.
Hybrid Isolation Forest - Application to Intrusion Detection
Authors: Pierre-François Marteau, Saeid Soheily-Khah, Nicolas Béchet
Date: 2017-05-10 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, Intrusion Detection, Network Intrusion Detection

From the identification of a drawback in the Isolation Forest (IF) algorithm
that limits its use in the scope of anomaly detection, we propose two
extensions that allow to firstly overcome the previously mention limitation and
secondly to provide it with some supervised learning capability. The resulting
Hybrid Isolation Forest (HIF) that we propose is first evaluated on a synthetic
dataset to analyze the effect of the new meta-parameters that are introduced
and verify that the addressed limitation of the IF algorithm is effectively
overcame. We hen compare the two algorithms on the ISCX benchmark dataset, in
the context of a network intrusion detection application. Our experiments show
that HIF outperforms IF, but also challenges the 1-class and 2-classes SVM
baselines with computational efficiency.
Flexible and Creative Chinese Poetry Generation Using Neural Memory
Authors: Jiyuan Zhang, Yang Feng, Dong Wang, Yang Wang, Andrew Abel, Shiyue Zhang, Andi Zhang
Date: 2017-05-10 00:00:00
Proceeding: ACL 2017 7

It has been shown that Chinese poems can be successfully generated by
sequence-to-sequence neural models, particularly with the attention mechanism.
A potential problem of this approach, however, is that neural models can only
learn abstract rules, while poem generation is a highly creative process that
involves not only rules but also innovations for which pure statistical models
are not appropriate in principle. This work proposes a memory-augmented neural
model for Chinese poem generation, where the neural model and the augmented
memory work together to balance the requirements of linguistic accordance and
aesthetic innovation, leading to innovative generations that are still
rule-compliant. In addition, it is found that the memory mechanism provides
interesting flexibility that can be used to generate poems with different
styles.
Comments on the proof of adaptive submodular function minimization
Authors: Feng Nan, Venkatesh Saligrama
Date: 2017-05-10 00:00:00
Tasks: Active Learning, Stochastic Optimization

We point out an issue with Theorem 5 appearing in "Group-based active query
selection for rapid diagnosis in time-critical situations". Theorem 5 bounds
the expected number of queries for a greedy algorithm to identify the class of
an item within a constant factor of optimal. The Theorem is based on
correctness of a result on minimization of adaptive submodular functions. We
present an example that shows that a critical step in Theorem A.11 of "Adaptive
Submodularity: Theory and Applications in Active Learning and Stochastic
Optimization" is incorrect.
Deep Label Distribution Learning with Label Ambiguity
Authors: Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, Xin Geng
Date: 2016-11-06 00:00:00
Tasks: Age Estimation, Classification, General Classification, Head Pose Estimation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Pose Estimation, Semantic Segmentation

Convolutional Neural Networks (ConvNets) have achieved excellent recognition
performance in various visual recognition tasks. A large labeled training set
is one of the most important factors for its success. However, it is difficult
to collect sufficient training images with precise labels in some domains such
as apparent age estimation, head pose estimation, multi-label classification
and semantic segmentation. Fortunately, there is ambiguous information among
labels, which makes these tasks different from traditional classification.
Based on this observation, we convert the label of each image into a discrete
label distribution, and learn the label distribution by minimizing a
Kullback-Leibler divergence between the predicted and ground-truth label
distributions using deep ConvNets. The proposed DLDL (Deep Label Distribution
Learning) method effectively utilizes the label ambiguity in both feature
learning and classifier learning, which help prevent the network from
over-fitting even when the training set is small. Experimental results show
that the proposed approach produces significantly better results than
state-of-the-art methods for age estimation and head pose estimation. At the
same time, it also improves recognition performance for multi-label
classification and semantic segmentation tasks.
Net2Vec: Deep Learning for the Network
Authors: Roberto Gonzalez, Filipe Manco, Alberto Garcia-Duran, Jose Mendes, Felipe Huici, Saverio Niccolini, Mathias Niepert
Date: 2017-05-10 00:00:00
Tasks: Deep Learning, General Classification, Traffic Classification

We present Net2Vec, a flexible high-performance platform that allows the
execution of deep learning algorithms in the communication network. Net2Vec is
able to capture data from the network at more than 60Gbps, transform it into
meaningful tuples and apply predictions over the tuples in real time. This
platform can be used for different purposes ranging from traffic classification
to network performance analysis.
  Finally, we showcase the use of Net2Vec by implementing and testing a
solution able to profile network users at line rate using traces coming from a
real network. We show that the use of deep learning for this case outperforms
the baseline method both in terms of accuracy and performance.
A Survey of Distant Supervision Methods using PGMs
Authors: Gagan Madan
Date: 2017-05-10 00:00:00
Tasks: Relation, Relation Extraction, Survey

Relation Extraction refers to the task of populating a database with tuples
of the form $r(e_1, e_2)$, where $r$ is a relation and $e_1$, $e_2$ are
entities. Distant supervision is one such technique which tries to
automatically generate training examples based on an existing KB such as
Freebase. This paper is a survey of some of the techniques in distant
supervision which primarily rely on Probabilistic Graphical Models (PGMs).
A Deep Learning Perspective on the Origin of Facial Expressions
Authors: Ran Breuer, Ron Kimmel
Date: 2017-05-04 00:00:00
Tasks: Deep Learning, Transfer Learning

Facial expressions play a significant role in human communication and
behavior. Psychologists have long studied the relationship between facial
expressions and emotions. Paul Ekman et al., devised the Facial Action Coding
System (FACS) to taxonomize human facial expressions and model their behavior.
The ability to recognize facial expressions automatically, enables novel
applications in fields like human-computer interaction, social gaming, and
psychological research. There has been a tremendously active research in this
field, with several recent papers utilizing convolutional neural networks (CNN)
for feature extraction and inference. In this paper, we employ CNN
understanding methods to study the relation between the features these
computational networks are using, the FACS and Action Units (AU). We verify our
findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets.
We apply these models to various tasks and tests using transfer learning,
including cross-dataset validation and cross-task performance. Finally, we
exploit the nature of the FER based CNN models for the detection of
micro-expressions and achieve state-of-the-art accuracy using a simple
long-short-term-memory (LSTM) recurrent neural network (RNN).
Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks
Authors: Sung-Ho Bae, Mohamed Elgharib, Mohamed Hefeeda, Wojciech Matusik
Date: 2017-05-10 00:00:00

Single-image-based view generation (SIVG) is important for producing 3D stereoscopic content. Here, handling different spatial resolutions as input and optimizing both reconstruction accuracy and processing speed is desirable. Latest approaches are based on convolutional neural network (CNN), and they generate promising results. However, their use of fully connected layers as well as pre-trained VGG forces a compromise between reconstruction accuracy and processing speed. In addition, this approach is limited to the use of a specific spatial resolution. To remedy these problems, we propose exploiting fully convolutional networks (FCN) for SIVG. We present two FCN architectures for SIVG. The first one is based on combination of an FCN and a view-rendering network called DeepView$_{ren}$. The second one consists of decoupled networks for luminance and chrominance signals, denoted by DeepView$_{dec}$. To train our solutions we present a large dataset of 2M stereoscopic images. Results show that both of our architectures improve accuracy and speed over the state of the art. DeepView$_{ren}$ generates competitive accuracy to the state of the art, however, with the fastest processing speed of all. That is x5 times faster speed and x24 times lower memory consumption compared to the state of the art. DeepView$_{dec}$ has much higher accuracy, but with x2.5 times faster speed and x12 times lower memory consumption. We evaluated our approach with both objective and subjective studies.
Reflection Separation Using Guided Annotation
Authors: Ofer Springer, Yair Weiss
Date: 2017-02-20 00:00:00

Photographs taken through a glass surface often contain an approximately
linear superposition of reflected and transmitted layers. Decomposing an image
into these layers is generally an ill-posed task and the use of an additional
image prior and user provided cues is presently necessary in order to obtain
good results. Current annotation approaches rely on a strong sparsity
assumption. For images with significant texture this assumption does not
typically hold, thus rendering the annotation process unviable. In this paper
we show that using a Gaussian Mixture Model patch prior, the correct local
decomposition can almost always be found as one of 100 likely modes of the
posterior. Thus, the user need only choose one of these modes in a sparse set
of patches and the decomposition may then be completed automatically. We
demonstrate the performance of our method using synthesized and real reflection
images.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Authors: Babak Ehteshami Bejnordi, Guido Zuidhof, Maschenka Balkenhol, Meyke Hermsen, Peter Bult, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak
Date: 2017-05-10 00:00:00
Tasks: Binary Classification, Classification, General Classification, whole slide images

Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first
train a CNN using high pixel resolution patches to capture cellular level
information. The feature responses generated by this model are then fed as
input to a second CNN, stacked on top of the first. Training of this stacked
architecture with large input patches enables learning of fine-grained
(cellular) details and global interdependence of tissue structures. Our system
is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast
tissue specimens. The system achieves an AUC of 0.962 for the binary
classification of non-malignant and malignant slides and obtains a three class
accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,
demonstrating its potentials for routine diagnostics.
A Biomedical Information Extraction Primer for NLP Researchers
Authors: Surag Nair
Date: 2017-05-10 00:00:00
Tasks: NER, Relation, Relation Extraction

Biomedical Information Extraction is an exciting field at the crossroads of
Natural Language Processing, Biology and Medicine. It encompasses a variety of
different tasks that require application of state-of-the-art NLP techniques,
such as NER and Relation Extraction. This paper provides an overview of the
problems in the field and discusses some of the techniques used for solving
them.
Mutual Kernel Matrix Completion
Authors: Tsuyoshi Kato, Rachelle Rivero
Date: 2017-02-14 00:00:00
Tasks: Matrix Completion

With the huge influx of various data nowadays, extracting knowledge from them
has become an interesting but tedious task among data scientists, particularly
when the data come in heterogeneous form and have missing information. Many
data completion techniques had been introduced, especially in the advent of
kernel methods. However, among the many data completion techniques available in
the literature, studies about mutually completing several incomplete kernel
matrices have not been given much attention yet. In this paper, we present a
new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that
tackles this problem of mutually inferring the missing entries of multiple
kernel matrices by combining the notions of data fusion and kernel matrix
completion, applied on biological data sets to be used for classification task.
We first introduced an objective function that will be minimized by exploiting
the EM algorithm, which in turn results to an estimate of the missing entries
of the kernel matrices involved. The completed kernel matrices are then
combined to produce a model matrix that can be used to further improve the
obtained estimates. An interesting result of our study is that the E-step and
the M-step are given in closed form, which makes our algorithm efficient in
terms of time and memory. After completion, the (completed) kernel matrices are
then used to train an SVM classifier to test how well the relationships among
the entries are preserved. Our empirical results show that the proposed
algorithm bested the traditional completion techniques in preserving the
relationships among the data points, and in accurately recovering the missing
kernel matrix entries. By far, MKMC offers a promising solution to the problem
of mutual estimation of a number of relevant incomplete kernel matrices.
Deep Speaker Feature Learning for Text-independent Speaker Verification
Authors: Lantian Li, Yixiang Chen, Ying Shi, Zhiyuan Tang, Dong Wang
Date: 2017-05-10 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw features. This paper presents a convolutional time-delay deep
neural network structure (CT-DNN) for speaker feature learning. Our
experimental results on the Fisher database demonstrated that this CT-DNN can
produce high-quality speaker features: even with a single feature (0.3 seconds
including the context), the EER can be as low as 7.68%. This effectively
confirmed that the speaker trait is largely a deterministic short-time property
rather than a long-time distributional pattern, and therefore can be extracted
from just dozens of frames.