pwc-archive/papers-with-abstracts huggingface dataset:
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Authors: Katharina Kann, Manuel Mager, Ivan Meza-Ruiz, Hinrich Schütze
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Cross-Lingual Transfer, Data Augmentation, Segmentation

Morphological segmentation for polysynthetic languages is challenging,
because a word may consist of many individual morphemes and training data can
be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define
the state of the art for morphological segmentation in high-resource settings
and for (mostly) European languages, we first show that they also obtain
competitive performance for Mexican polysynthetic languages in minimal-resource
settings. We then propose two novel multi-task training approaches -one with,
one without need for external unlabeled resources-, and two corresponding data
augmentation methods, improving over the neural baseline for all languages.
Finally, we explore cross-lingual transfer as a third way to fortify our neural
model and show that we can train one single multi-lingual model for related
languages while maintaining comparable or even improved performance, thus
reducing the amount of parameters by close to 75%. We provide our morphological
segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for
future research.
DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor
Authors: Tao Yu, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Qionghai Dai, Hao Li, Gerard Pons-Moll, Yebin Liu
Date: 2018-04-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Dynamic Reconstruction

We propose DoubleFusion, a new real-time system that combines volumetric
dynamic reconstruction with data-driven template fitting to simultaneously
reconstruct detailed geometry, non-rigid motion and the inner human body shape
from a single depth camera. One of the key contributions of this method is a
double layer representation consisting of a complete parametric body shape
inside, and a gradually fused outer surface layer. A pre-defined node graph on
the body surface parameterizes the non-rigid deformations near the body, and a
free-form dynamically changing graph parameterizes the outer surface layer far
from the body, which allows more general reconstruction. We further propose a
joint motion tracking method based on the double layer representation to enable
robust and fast motion tracking performance. Moreover, the inner body shape is
optimized online and forced to fit inside the outer surface layer. Overall, our
method enables increasingly denoised, detailed and complete surface
reconstructions, fast motion tracking performance and plausible inner body
shape reconstruction in real-time. In particular, experiments show improved
fast motion tracking and loop closure performance on more challenging
scenarios.
Model-Free Linear Quadratic Control via Reduction to Expert Prediction
Authors: Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari
Date: 2018-04-17 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Model-free approaches for reinforcement learning (RL) and continuous control
find policies based only on past states and rewards, without fitting a model of
the system dynamics. They are appealing as they are general purpose and easy to
implement; however, they also come with fewer theoretical guarantees than
model-based RL. In this work, we present a new model-free algorithm for
controlling linear quadratic (LQ) systems, and show that its regret scales as
$O(T^{\xi+2/3})$ for any small $\xi>0$ if time horizon satisfies $T>C^{1/\xi}$
for a constant $C$. The algorithm is based on a reduction of control of Markov
decision processes to an expert prediction problem. In practice, it corresponds
to a variant of policy iteration with forced exploration, where the policy in
each phase is greedy with respect to the average of all previous value
functions. This is the first model-free algorithm for adaptive control of LQ
systems that provably achieves sublinear regret and has a polynomial
computation cost. Empirically, our algorithm dramatically outperforms standard
policy iteration, but performs worse than a model-based approach.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
Authors: Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Relation, Relation Extraction, Temporal Relation Extraction

Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available.
EVA$^2$: Exploiting Temporal Redundancy in Live Computer Vision
Authors: Mark Buckler, Philip Bedoukian, Suren Jayasuriya, Adrian Sampson
Date: 2018-03-16 00:00:00
Tasks: Motion Compensation, Motion Estimation, Video Compression

Hardware support for deep convolutional neural networks (CNNs) is critical to
advanced computer vision in mobile and embedded devices. Current designs,
however, accelerate generic CNNs; they do not exploit the unique
characteristics of real-time vision. We propose to use the temporal redundancy
in natural video to avoid unnecessary computation on most frames. A new
algorithm, activation motion compensation, detects changes in the visual input
and incrementally updates a previously-computed output. The technique takes
inspiration from video compression and applies well-known motion estimation
techniques to adapt to visual changes. We use an adaptive key frame rate to
control the trade-off between efficiency and vision quality as the input
changes. We implement the technique in hardware as an extension to existing
state-of-the-art CNN accelerator designs. The new unit reduces the average
energy per frame by 54.2%, 61.7%, and 87.6% for three CNNs with less than 1%
loss in vision accuracy.
Universal Neural Machine Translation for Extremely Low Resource Languages
Authors: Jiatao Gu, Hany Hassan, Jacob Devlin, Victor O. K. Li
Date: 2018-02-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Sentence, Transfer Learning, Translation

In this paper, we propose a new universal machine translation approach
focusing on languages with a limited amount of parallel data. Our proposed
approach utilizes a transfer-learning approach to share lexical and sentence
level representations across multiple source languages into one target
language. The lexical part is shared through a Universal Lexical Representation
to support multilingual word-level sharing. The sentence-level sharing is
represented by a model of experts from all source languages that share the
source encoders with all other languages. This enables the low-resource
language to utilize the lexical and sentence representations of the higher
resource languages. Our approach is able to achieve 23 BLEU on Romanian-English
WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU
of strong baseline system which uses multilingual training and
back-translation. Furthermore, we show that the proposed approach can achieve
almost 20 BLEU on the same dataset through fine-tuning a pre-trained
multi-lingual system in a zero-shot setting.
Block Mean Approximation for Efficient Second Order Optimization
Authors: Yao Lu, Mehrtash Harandi, Richard Hartley, Razvan Pascanu
Date: 2018-04-16 00:00:00

Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates. At their heart, such algorithms need to compute the inverse or inverse square root of a matrix whose size is quadratic of the dimensionality of the search space. For high dimensional search spaces, the matrix inversion or inversion of square root becomes overwhelming which in turn demands for approximate methods. In this work, we propose a new matrix approximation method which divides a matrix into blocks and represents each block by one or two numbers. The method allows efficient computation of matrix inverse and inverse square root. We apply our method to AdaGrad in training deep neural networks. Experiments show encouraging results compared to the diagonal approximation.
Classifying magnetic resonance image modalities with convolutional neural networks
Authors: Samuel Remedios, Dzung L. Pham, John A. Butman, Snehashis Roy
Date: 2018-04-16 00:00:00
Tasks: Content-Based Image Retrieval, General Classification, Image Retrieval, Retrieval

Magnetic Resonance (MR) imaging allows the acquisition of images with
different contrast properties depending on the acquisition protocol and the
magnetic properties of tissues. Many MR brain image processing techniques, such
as tissue segmentation, require multiple MR contrasts as inputs, and each
contrast is treated differently. Thus it is advantageous to automate the
identification of image contrasts for various purposes, such as facilitating
image processing pipelines, and managing and maintaining large databases via
content-based image retrieval (CBIR). Most automated CBIR techniques focus on a
two-step process: extracting features from data and classifying the image based
on these features. We present a novel 3D deep convolutional neural network
(CNN)-based method for MR image contrast classification. The proposed CNN
automatically identifies the MR contrast of an input brain image volume.
Specifically, we explored three classification problems: (1) identify
T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery
(FLAIR) contrasts, (2) identify pre vs post-contrast T1, (3) identify pre vs
post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites
and multiple scanners were used. To evaluate each task, the proposed model was
trained on 2137 images and tested on the remaining 1281 images. Results showed
that image volumes were correctly classified with 97.57% accuracy.
Geometry-aware Deep Network for Single-Image Novel View Synthesis
Authors: Miaomiao Liu, Xuming He, Mathieu Salzmann
Date: 2018-04-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D geometry, Novel View Synthesis

This paper tackles the problem of novel view synthesis from a single image.
In particular, we target real-world scenes with rich geometric structure, a
challenging task due to the large appearance variations of such scenes and the
lack of simple 3D models to represent them. Modern, learning-based approaches
mostly focus on appearance to synthesize novel views and thus tend to generate
predictions that are inconsistent with the underlying scene structure. By
contrast, in this paper, we propose to exploit the 3D geometry of the scene to
synthesize a novel view. Specifically, we approximate a real-world scene by a
fixed number of planes, and learn to predict a set of homographies and their
corresponding region masks to transform the input image into a novel view. To
this end, we develop a new region-aware geometric transform network that
performs these multiple tasks in a common framework. Our results on the outdoor
KITTI and the indoor ScanNet datasets demonstrate the effectiveness of our
network in generating high quality synthetic views that respect the scene
geometry, thus outperforming the state-of-the-art methods.
Neural Compatibility Modeling with Attentive Knowledge Distillation
Authors: Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, Liqiang Nie
Date: 2018-04-17 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, speech-recognition, Speech Recognition

Recently, the booming fashion sector and its huge potential benefits have
attracted tremendous attention from many research communities. In particular,
increasing research efforts have been dedicated to the complementary clothing
matching as matching clothes to make a suitable outfit has become a daily
headache for many people, especially those who do not have the sense of
aesthetics. Thanks to the remarkable success of neural networks in various
applications such as image classification and speech recognition, the
researchers are enabled to adopt the data-driven learning methods to analyze
fashion items. Nevertheless, existing studies overlook the rich valuable
knowledge (rules) accumulated in fashion domain, especially the rules regarding
clothing matching. Towards this end, in this work, we shed light on
complementary clothing matching by integrating the advanced deep neural
networks and the rich fashion domain knowledge. Considering that the rules can
be fuzzy and different rules may have different confidence levels to different
samples, we present a neural compatibility modeling scheme with attentive
knowledge distillation based on the teacher-student network scheme. Extensive
experiments on the real-world dataset show the superiority of our model over
several state-of-the-art baselines. Based upon the comparisons, we observe
certain fashion insights that add value to the fashion matching study. As a
byproduct, we released the codes, and involved parameters to benefit other
researchers.
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses
Authors: Katherine A. Keith, Su Lin Blodgett, Brendan O'Connor
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, Sentence

Dependency parsing research, which has made significant gains in recent
years, typically focuses on improving the accuracy of single-tree predictions.
However, ambiguity is inherent to natural language syntax, and communicating
such ambiguity is important for error analysis and better-informed downstream
applications. In this work, we propose a transition sampling algorithm to
sample from the full joint distribution of parse trees defined by a
transition-based parsing model, and demonstrate the use of the samples in
probabilistic dependency analysis. First, we define the new task of dependency
path prediction, inferring syntactic substructures over part of a sentence, and
provide the first analysis of performance on this task. Second, we demonstrate
the usefulness of our Monte Carlo syntax marginal method for parser error
analysis and calibration. Finally, we use this method to propagate parse
uncertainty to two downstream information extraction applications: identifying
persons killed by police and semantic role assignment.
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Authors: Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour
Date: 2018-03-28 00:00:00
Tasks: Data Augmentation, Image Segmentation, Lesion Detection, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Fully convolutional deep neural networks have been asserted to be fast and
precise frameworks with great potential in image segmentation. One of the major
challenges in training such networks raises when data is unbalanced, which is
common in many medical imaging applications such as lesion segmentation where
lesion class voxels are often much lower in numbers than non-lesion voxels. A
trained network with unbalanced data may make predictions with high precision
and low recall, being severely biased towards the non-lesion class which is
particularly undesired in most medical applications where FNs are more
important than FPs. Various methods have been proposed to address this problem,
more recently similarity loss functions and focal loss. In this work we trained
fully convolutional deep neural networks using an asymmetric similarity loss
function to mitigate the issue of data imbalance and achieve much better
tradeoff between precision and recall. To this end, we developed a 3D
FC-DenseNet with large overlapping image patches as input and an asymmetric
similarity loss layer based on Tversky index (using Fbeta scores). We used
large overlapping image patches as inputs for intrinsic and extrinsic data
augmentation, a patch selection algorithm, and a patch prediction fusion
strategy using B-spline weighted soft voting to account for the uncertainty of
prediction in patch borders. We applied this method to MS lesion segmentation
based on two different datasets of MSSEG and ISBI longitudinal MS lesion
segmentation challenge, where we achieved top performance in both challenges.
Our network trained with focal loss ranked first according to the ISBI
challenge overall score and resulted in the lowest reported lesion false
positive rate among all submitted methods. Our network trained with the
asymmetric similarity loss led to the lowest surface distance and the best
lesion true positive rate.
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
Authors: Yanghoon Kim, Hwanhee Lee, Kyomin Jung
Date: 2018-04-03 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, General Classification, Sentence

In this paper, we propose an attention-based classifier that predicts
multiple emotions of a given sentence. Our model imitates human's two-step
procedure of sentence understanding and it can effectively represent and
classify sentences. With emoji-to-meaning preprocessing and extra lexicon
utilization, we further improve the model performance. We train and evaluate
our model with data provided by SemEval-2018 task 1-5, each sentence of which
has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank
in English/Spanish respectively.
Learning Joint Semantic Parsers from Disjoint Data
Authors: Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, Semantic Dependency Parsing, Semantic Parsing

We present a new approach to learning semantic parsers from multiple
datasets, even when the target semantic formalisms are drastically different,
and the underlying corpora do not overlap. We handle such "disjoint" data by
treating annotations for unobserved formalisms as latent structured variables.
Building on state-of-the-art baselines, we show improvements both in
frame-semantic parsing and semantic dependency parsing by modeling them
jointly.
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Authors: Shuai Li, Dinei Florencio, Wanqing Li, Yaqin Zhao, Chris Cook
Date: 2018-04-16 00:00:00

Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.
Controlling the Charging of Electric Vehicles with Neural Networks
Authors: Martin Pilát
Date: 2018-04-16 00:00:00

We propose and evaluate controllers for the coordination of the charging of
electric vehicles. The controllers are based on neural networks and are
completely de-centralized, in the sense that the charging current is completely
decided by the controller itself. One of the versions of the controllers does
not require any outside communication at all.
  We test controllers based on two different architectures of neural networks -
the feed-forward networks and the echo state networks. The networks are
optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based
method. The results of the different architectures and the different
optimization algorithms are compared in a realistic scenario. We show that the
controllers are able to charge the cars while keeping the peak consumptions
almost the same as when no charging is performed. Moreover, the controllers
fill the valleys of the consumption thus reducing the difference between the
maximum and minimum consumption in the grid.
A Deeper Look into Dependency-Based Word Embeddings
Authors: Sean MacAvaney, Amir Zeldes
Date: 2018-04-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Word Embeddings, Word Similarity

We investigate the effect of various dependency-based word embeddings on
distinguishing between functional and domain similarity, word similarity
rankings, and two downstream tasks in English. Variations include word
embeddings trained using context windows from Stanford and Universal
dependencies at several levels of enhancement (ranging from unlabeled, to
Enhanced++ dependencies). Results are compared to basic linear contexts and
evaluated on several datasets. We found that embeddings trained with Universal
and Stanford dependency contexts excel at different tasks, and that enhanced
dependencies often improve performance.
FEVER: a large-scale dataset for Fact Extraction and VERification
Authors: James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
Date: 2018-03-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Claim Verification, Sentence

In this paper we introduce a new publicly available dataset for verification
against textual sources, FEVER: Fact Extraction and VERification. It consists
of 185,445 claims generated by altering sentences extracted from Wikipedia and
subsequently verified without knowledge of the sentence they were derived from.
The claims are classified as Supported, Refuted or NotEnoughInfo by annotators
achieving 0.6841 in Fleiss $\kappa$. For the first two classes, the annotators
also recorded the sentence(s) forming the necessary evidence for their
judgment. To characterize the challenge of the dataset presented, we develop a
pipeline approach and compare it to suitably designed oracles. The best
accuracy we achieve on labeling a claim accompanied by the correct evidence is
31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that
FEVER is a challenging testbed that will help stimulate progress on claim
verification against textual sources.