pwc-archive/papers-with-abstracts huggingface dataset:
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
Authors: Anusri Pampari, Preethi Raghavan, Jennifer Liang, Jian Peng
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Form, Question Answering

We propose a novel methodology to generate domain-specific large-scale
question answering (QA) datasets by re-purposing existing annotations for other
NLP tasks. We demonstrate an instance of this methodology in generating a
large-scale QA dataset for electronic medical records by leveraging existing
expert annotations on clinical notes for various NLP tasks from the community
shared i2b2 datasets. The resulting corpus (emrQA) has 1 million
question-logical form and 400,000+ question-answer evidence pairs. We
characterize the dataset and explore its learning potential by training
baseline models for question to logical form and question to answer mapping.
Estimating Small Differences in Car-Pose from Orbits
Authors: Berkay Kicanaoglu, Ran Tao, Arnold W. M. Smeulders
Date: 2018-09-03 00:00:00
Tasks: Object

Distinction among nearby poses and among symmetries of an object is
challenging. In this paper, we propose a unified, group-theoretic approach to
tackle both. Different from existing works which directly predict absolute
pose, our method measures the pose of an object relative to another pose, i.e.,
the pose difference. The proposed method generates the complete orbit of an
object from a single view of the object with respect to the subgroup of SO(3)
of rotations around the z-axis, and compares the orbit of the object with
another orbit using a novel orbit metric to estimate the pose difference. The
generated orbit in the latent space records all the differences in pose in the
original observational space, and as a result, the method is capable of finding
subtle differences in pose. We demonstrate the effectiveness of the proposed
method on cars, where identifying the subtle pose differences is vital.
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
Authors: Alexandra Chronopoulou, Aikaterini Margatina, Christos Baziotis, Alexandros Potamianos
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, General Classification, Sentiment Analysis, Transfer Learning, Word Embeddings

In this paper we present our approach to tackle the Implicit Emotion Shared
Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from
which a certain word has been removed, we are asked to predict the emotion of
the missing word. In this work, we experiment with neural Transfer Learning
(TL) methods. Our models are based on LSTM networks, augmented with a
self-attention mechanism. We use the weights of various pretrained models, for
initializing specific layers of our networks. We leverage a big collection of
unlabeled Twitter messages, for pretraining word2vec word embeddings and a set
of diverse language models. Moreover, we utilize a sentiment analysis dataset
for pretraining a model, which encodes emotion related information. The
submitted model consists of an ensemble of the aforementioned TL models. Our
team ranked 3rd out of 30 participants, achieving an F1 score of 0.703.
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Authors: Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger
Date: 2018-09-03 00:00:00
Tasks: Benchmarking, Simultaneous Localization and Mapping

Datasets have gained an enormous amount of popularity in the computer vision
community, from training and evaluation of Deep Learning-based methods to
benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt,
synthetic imagery bears a vast potential due to scalability in terms of amounts
of data obtainable without tedious manual ground truth annotations or
measurements. Here, we present a dataset with the aim of providing a higher
degree of photo-realism, larger scale, more variability as well as serving a
wider range of purposes compared to existing datasets. Our dataset leverages
the availability of millions of professional interior designs and millions of
production-level furniture and object assets -- all coming with fine geometric
details and high-resolution texture. We render high-resolution and high
frame-rate video sequences following realistic trajectories while supporting
various camera types as well as providing inertial measurements. Together with
the release of the dataset, we will make executable program of our interactive
simulator software as well as our renderer available at
https://interiornetdataset.github.io. To showcase the usability and uniqueness
of our dataset, we show benchmarking results of both sparse and dense SLAM
algorithms.
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks
Authors: César A. Uribe, Soomin Lee, Alexander Gasnikov, Angelia Nedić
Date: 2018-09-03 00:00:00
Tasks: Distributed Optimization

We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum $\sum_{i=1}^{m}f_i(z)$ of functions over in a network. We provide complexity bounds for four different cases, namely: each function $f_i$ is strongly convex and smooth, each function is either strongly convex or smooth, and when it is convex but neither strongly convex nor smooth. Our approach is based on the dual of an appropriately formulated primal problem, which includes a graph that models the communication restrictions. We propose distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional optimal cost related to the spectral properties of the network. Initially, we focus on functions for which we can explicitly minimize its Legendre-Fenchel conjugate, i.e., admissible or dual friendly functions. Then, we study distributed optimization algorithms for non-dual friendly functions, as well as a method to improve the dependency on the parameters of the functions involved. Numerical analysis of the proposed algorithms is also provided.
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction
Authors: Jinhua Du, Jingguang Han, Andy Way, Dadong Wan
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Relation, Relation Extraction, Sentence, valid

Attention mechanisms are often used in deep neural networks for distantly
supervised relation extraction (DS-RE) to distinguish valid from noisy
instances. However, traditional 1-D vector attention models are insufficient
for the learning of different contexts in the selection of valid instances to
predict the relationship for an entity pair. To alleviate this issue, we
propose a novel multi-level structured (2-D matrix) self-attention mechanism
for DS-RE in a multi-instance learning (MIL) framework using bidirectional
recurrent neural networks. In the proposed method, a structured word-level
self-attention mechanism learns a 2-D matrix where each row vector represents a
weight distribution for different aspects of an instance regarding two
entities. Targeting the MIL issue, the structured sentence-level attention
learns a 2-D matrix where each row vector represents a weight distribution on
selection of different valid in-stances. Experiments conducted on two publicly
available DS-RE datasets show that the proposed framework with a multi-level
structured self-attention mechanism significantly outperform state-of-the-art
baselines in terms of PR curves, P@N and F1 measures.
Convolutional Neural Network for Trajectory Prediction
Authors: Nishant Nikhil, Brendan Tran Morris
Date: 2018-09-03 00:00:00
Tasks: Prediction, Trajectory Prediction

Predicting trajectories of pedestrians is quintessential for autonomous
robots which share the same environment with humans. In order to effectively
and safely interact with humans, trajectory prediction needs to be both precise
and computationally efficient. In this work, we propose a convolutional neural
network (CNN) based human trajectory prediction approach. Unlike more recent
LSTM-based moles which attend sequentially to each frame, our model supports
increased parallelism and effective temporal representation. The proposed
compact CNN model is faster than the current approaches yet still yields
competitive results.
Diverse and Coherent Paragraph Generation from Images
Authors: Moitreya Chatterjee, Alexander G. Schwing
Date: 2018-09-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diversity, Image Captioning, Image Paragraph Captioning, Video Summarization

Paragraph generation from images, which has gained popularity recently, is an
important task for video summarization, editing, and support of the disabled.
Traditional image captioning methods fall short on this front, since they
aren't designed to generate long informative descriptions. Moreover, the
vanilla approach of simply concatenating multiple short sentences, possibly
synthesized from a classical image captioning system, doesn't embrace the
intricacies of paragraphs: coherent sentences, globally consistent structure,
and diversity. To address those challenges, we propose to augment paragraph
generation techniques with 'coherence vectors', 'global topic vectors', and
modeling of the inherent ambiguity of associating paragraphs with images, via a
variational auto-encoder formulation. We demonstrate the effectiveness of the
developed approach on two datasets, outperforming existing state-of-the-art
techniques on both.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
Authors: Jiuniu Wang, Xingyu Fu, Guangluan Xu, Yirong Wu, Ziyan Chen, Yang Wei, Li Jin
Date: 2018-09-03 00:00:00
Tasks: Diversity, Machine Reading Comprehension, Reading Comprehension

In this paper, we introduce Adversarial-and-attention Network (A3Net) for
Machine Reading Comprehension. This model extends existing approaches from two
perspectives. First, adversarial training is applied to several target
variables within the model, rather than only to the inputs or embeddings. We
control the norm of adversarial perturbations according to the norm of original
target variables, so that we can jointly add perturbations to several target
variables during training. As an effective regularization method, adversarial
training improves robustness and generalization of our model. Second, we
propose a multi-layer attention network utilizing three kinds of
high-efficiency attention mechanisms. Multi-layer attention conducts
interaction between question and passage within each layer, which contributes
to reasonable representation and understanding of the model. Combining these
two contributions, we enhance the diversity of dataset and the information
extracting ability of the model at the same time. Meanwhile, we construct A3Net
for the WebQA dataset. Results show that our model outperforms the
state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%).
Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning
Authors: Junjun Jiang, Yi Yu, Suhua Tang, Jiayi Ma, Akiko Aizawa, Kiyoharu Aizawa
Date: 2018-09-03 00:00:00
Tasks: Face Hallucination, Hallucination, Position

Face hallucination is a technique that reconstruct high-resolution (HR) faces
from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR
face pairs. Most state-of-the-arts leverage position-patch prior knowledge of
human face to estimate the optimal representation coefficients for each image
patch. However, they focus only the position information and usually ignore the
context information of image patch. In addition, when they are confronted with
misalignment or the Small Sample Size (SSS) problem, the hallucination
performance is very poor. To this end, this study incorporates the contextual
information of image patch and proposes a powerful and efficient context-patch
based face hallucination approach, namely Thresholding Locality-constrained
Representation and Reproducing learning (TLcR-RL). Under the context-patch
based framework, we advance a thresholding based representation method to
enhance the reconstruction accuracy and reduce the computational complexity. To
further improve the performance of the proposed algorithm, we propose a
promotion strategy called reproducing learning. By adding the estimated HR face
to the training set, which can simulates the case that the HR version of the
input LR face is present in the training set, thus iteratively enhancing the
final hallucination result. Experiments demonstrate that the proposed TLcR-RL
method achieves a substantial increase in the hallucinated results, both
subjectively and objectively. Additionally, the proposed framework is more
robust to face misalignment and the SSS problem, and its hallucinated HR face
is still very good when the LR test face is from the real-world. The MATLAB
source code is available at https://github.com/junjun-jiang/TLcR-RL
Exploring the Landscape of Relational Syllogistic Logics
Authors: Alex Kruckman, Lawrence S. Moss
Date: 2018-09-03 00:00:00

This paper explores relational syllogistic logics, a family of logical systems related to reasoning about relations in extensions of the classical syllogistic. These are all decidable logical systems. We prove completeness theorems and complexity results for a natural subfamily of relational syllogistic logics, parametrized by constructors for terms and for sentences.
Towards Dynamic Computation Graphs via Sparse Latent Structure
Authors: Vlad Niculae, André F. T. Martins, Claire Cardie
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: graph construction

Deep NLP models benefit from underlying structures in the data---e.g., parse
trees---typically extracted using off-the-shelf parsers. Recent attempts to
jointly learn the latent structure encounter a tradeoff: either make
factorization assumptions that limit expressiveness, or sacrifice end-to-end
differentiability. Using the recently proposed SparseMAP inference, which
retrieves a sparse distribution over latent structures, we propose a novel
approach for end-to-end learning of latent structure predictors jointly with a
downstream predictor. To the best of our knowledge, our method is the first to
enable unrestricted dynamic computation graph construction from the global
latent structure, while maintaining differentiability.
Automatic Event Salience Identification
Authors: Zhengzhong Liu, Chenyan Xiong, Teruko Mitamura, Eduard Hovy
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10

Identifying the salience (i.e. importance) of discourse units is an important
task in language understanding. While events play important roles in text
documents, little research exists on analyzing their saliency status. This
paper empirically studies the Event Salience task and proposes two salience
detection models based on content similarities and discourse relations. The
first is a feature based salience model that incorporates similarities among
discourse units. The second is a neural model that captures more complex
relations between discourse units. Tested on our new large-scale event salience
corpus, both methods significantly outperform the strong frequency baseline,
while our neural model further improves the feature based one by a large
margin. Our analyses demonstrate that our neural model captures interesting
connections between salience and discourse unit relations (e.g., scripts and
frame structures).
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks
Authors: Zhixiang Hao, Yu Li, ShaoDi You, Feng Lu
Date: 2018-09-03 00:00:00
Tasks: Depth Estimation

Convolutional Neural Networks have demonstrated superior performance on
single image depth estimation in recent years. These works usually use stacked
spatial pooling or strided convolution to get high-level information which are
common practices in classification task. However, depth estimation is a dense
prediction problem and low-resolution feature maps usually generate blurred
depth map which is undesirable in application. In order to produce high quality
depth map, say clean and accurate, we propose a network consists of a Dense
Feature Extractor (DFE) and a Depth Map Generator (DMG). The DFE combines
ResNet and dilated convolutions. It extracts multi-scale information from input
image while keeping the feature maps dense. As for DMG, we use attention
mechanism to fuse multi-scale features produced in DFE. Our Network is trained
end-to-end and does not need any post-processing. Hence, it runs fast and can
predict depth map in about 15 fps. Experiment results show that our method is
competitive with the state-of-the-art in quantitative evaluation, but can
preserve better structural details of the scene depth.
Learning Saliency Prediction From Sparse Fixation Pixel Map
Authors: Shanghua Xiao
Date: 2018-09-03 00:00:00
Tasks: Clustering, Prediction, Saliency Prediction

Ground truth for saliency prediction datasets consists of two types of map
data: fixation pixel map which records the human eye movements on sample
images, and fixation blob map generated by performing gaussian blurring on the
corresponding fixation pixel map. Current saliency approaches perform
prediction by directly pixel-wise regressing the input image into saliency map
with fixation blob as ground truth, yet learning saliency from fixation pixel
map is not explored. In this work, we propose a first-of-its-kind approach of
learning saliency prediction from sparse fixation pixel map, and a novel loss
function for training from such sparse fixation. We utilize clustering to
extract sparse fixation pixel from the raw fixation pixel map, and add a
max-pooling transformation on the output to avoid false penalty between sparse
outputs and labels caused by nearby but non-overlapping saliency pixels when
calculating loss. This approach provides a novel perspective for achieving
saliency prediction. We evaluate our approach over multiple benchmark datasets,
and achieve competitive performance in terms of multiple metrics comparing with
state-of-the-art saliency methods.
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Authors: Lina Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai, Clare Mansfield, Osman Ramadan, Stefan Ultes, Michael Crawford, Milica Gasic
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 10
Tasks: Deep Learning, Sentence, Sentence Embeddings, Sentiment Analysis, Word Embeddings

In recent years, we have seen deep learning and distributed representations
of words and sentences make impact on a number of natural language processing
tasks, such as similarity, entailment and sentiment analysis. Here we introduce
a new task: understanding of mental health concepts derived from Cognitive
Behavioural Therapy (CBT). We define a mental health ontology based on the CBT
principles, annotate a large corpus where this phenomena is exhibited and
perform understanding using deep learning and distributed representations. Our
results show that the performance of deep learning models combined with word
embeddings or sentence embeddings significantly outperform non-deep-learning
models in this difficult task. This understanding module will be an essential
component of a statistical dialogue system delivering therapy.
Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques
Authors: Dorjan Hitaj, Luigi V. Mancini
Date: 2018-09-03 00:00:00
Tasks: BIG-bench Machine Learning

Deep neural networks have had enormous impact on various domains of computer
science, considerably outperforming previous state of the art machine learning
techniques. To achieve this performance, neural networks need large quantities
of data and huge computational resources, which heavily increases their
construction costs. The increased cost of building a good deep neural network
model gives rise to a need for protecting this investment from potential
copyright infringements. Legitimate owners of a machine learning model want to
be able to reliably track and detect a malicious adversary that tries to steal
the intellectual property related to the model. Recently, this problem was
tackled by introducing in deep neural networks the concept of watermarking,
which allows a legitimate owner to embed some secret information(watermark) in
a given model. The watermark allows the legitimate owner to detect copyright
infringements of his model. This paper focuses on verifying the robustness and
reliability of state-of- the-art deep neural network watermarking schemes. We
show that, a malicious adversary, even in scenarios where the watermark is
difficult to remove, can still evade the verification by the legitimate owners,
thus avoiding the detection of model theft.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
Authors: Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, Iryna Gurevych
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Claim Verification, Entity Linking, General Classification, Natural Language Inference, Retrieval, Sentence

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).
Image computing for fibre-bundle endomicroscopy: A review
Authors: Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin, Tom Vercauteren
Date: 2018-09-03 00:00:00
Tasks: Diagnostic, Image Reconstruction

Endomicroscopy is an emerging imaging modality, that facilitates the
acquisition of in vivo, in situ optical biopsies, assisting diagnostic and
potentially therapeutic interventions. While there is a diverse and constantly
expanding range of commercial and experimental optical biopsy platforms
available, fibre-bundle endomicroscopy is currently the most widely used
platform and is approved for clinical use in a range of clinical indications.
Miniaturised, flexible fibre-bundles, guided through the working channel of
endoscopes, needles and catheters, enable high-resolution imaging across a
variety of organ systems. Yet, the nature of image acquisition though a
fibre-bundle gives rise to several inherent characteristics and limitations
necessitating novel and effective image pre- and post-processing algorithms,
ranging from image formation, enhancement and mosaicing to pathology detection
and quantification. This paper introduces the underlying technology and most
prevalent clinical applications of fibre-bundle endomicroscopy, and provides a
comprehensive, up-to-date, review of relevant image reconstruction, analysis
and understanding/inference methodologies. Furthermore, current limitations as
well as future challenges and opportunities in fibre-bundle endomicroscopy
computing are identified and discussed.