pwc-archive/papers-with-abstracts huggingface dataset:
Identifying Subjective and Figurative Language in Online Dialogue
Authors: Stephanie M. Lukin, Luke Eisenberg, Thomas Corcoran, Marilyn A. Walker
Date: 2017-08-29 00:00:00
Tasks: Articles

More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic resources such as news, highly social
dialogue is very frequent in social media. We aim to automatically identify
sarcastic and nasty utterances in unannotated online dialogue, extending a
bootstrapping method previously applied to the classification of monologic
subjective sentences in Riloff and Weibe 2003. We have adapted the method to
fit the sarcastic and nasty dialogic domain. Our method is as follows: 1)
Explore methods for identifying sarcastic and nasty cue words and phrases in
dialogues; 2) Use the learned cues to train a sarcastic (nasty) Cue-Based
Classifier; 3) Learn general syntactic extraction patterns from the sarcastic
(nasty) utterances and define fine-tuned sarcastic patterns to create a
Pattern-Based Classifier; 4) Combine both Cue-Based and fine-tuned
Pattern-Based Classifiers to maximize precision at the expense of recall and
test on unannotated utterances.
A Challenge Set Approach to Evaluating Machine Translation
Authors: Pierre Isabelle, Colin Cherry, George Foster
Date: 2017-04-24 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Translation

Neural machine translation represents an exciting leap forward in translation
quality. But what longstanding weaknesses does it resolve, and which remain? We
address these questions with a challenge set approach to translation evaluation
and error analysis. A challenge set consists of a small set of sentences, each
hand-designed to probe a system's capacity to bridge a particular structural
divergence between languages. To exemplify this approach, we present an
English-French challenge set, and use it to analyze phrase-based and neural
systems. The resulting analysis provides not only a more fine-grained picture
of the strengths of neural systems, but also insight into which linguistic
phenomena remain out of reach.
Generating Different Story Tellings from Semantic Representations of Narrative
Authors: Elena Rishes, Stephanie M. Lukin, David K. Elson, Marilyn A. Walker
Date: 2017-08-29 00:00:00
Tasks: Text Generation

In order to tell stories in different voices for different audiences,
interactive story systems require: (1) a semantic representation of story
structure, and (2) the ability to automatically generate story and dialogue
from this semantic representation using some form of Natural Language
Generation (NLG). However, there has been limited research on methods for
linking story structures to narrative descriptions of scenes and story events.
In this paper we present an automatic method for converting from Scheherazade's
story intention graph, a semantic representation, to the input required by the
Personage NLG engine. Using 36 Aesop Fables distributed in DramaBank, a
collection of story encodings, we train translation rules on one story and then
test these rules by generating text for the remaining 35. The results are
measured in terms of the string similarity metrics Levenshtein Distance and
BLEU score. The results show that we can generate the 35 stories with correct
content: the test set stories on average are close to the output of the
Scheherazade realizer, which was customized to this semantic representation. We
provide some examples of story variations generated by personage. In future
work, we will experiment with measuring the quality of the same stories
generated in different voices, and with techniques for making storytelling
interactive.
Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue
Authors: Stephanie Lukin, Marilyn Walker
Date: 2017-08-29 00:00:00
Proceeding: WS 2013 6
Tasks: Articles

More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic Natural Language Processing resources such
as news, highly social dialogue is frequent in social media, making it a
challenging context for NLP. This paper tests a bootstrapping method,
originally proposed in a monologic domain, to train classifiers to identify two
different types of subjective language in dialogue: sarcasm and nastiness. We
explore two methods of developing linguistic indicators to be used in a first
level classifier aimed at maximizing precision at the expense of recall. The
best performing classifier for the first phase achieves 54% precision and 38%
recall for sarcastic utterances. We then use general syntactic patterns from
previous work to create more general sarcasm indicators, improving precision to
62% and recall to 52%. To further test the generality of the method, we then
apply it to bootstrapping a classifier for nastiness dialogic acts. Our first
phase, using crowdsourced nasty indicators, achieves 58% precision and 49%
recall, which increases to 75% precision and 62% recall when we bootstrap over
the first level with generalized syntactic patterns.
Leaf Counting with Deep Convolutional and Deconvolutional Networks
Authors: Shubhra Aich, Ian Stavness
Date: 2017-08-24 00:00:00
Tasks: Data Augmentation, Deep Learning, Plant Phenotyping

In this paper, we investigate the problem of counting rosette leaves from an
RGB image, an important task in plant phenotyping. We propose a data-driven
approach for this task generalized over different plant species and imaging
setups. To accomplish this task, we use state-of-the-art deep learning
architectures: a deconvolutional network for initial segmentation and a
convolutional network for leaf counting. Evaluation is performed on the leaf
counting challenge dataset at CVPPP-2017. Despite the small number of training
samples in this dataset, as compared to typical deep learning image sets, we
obtain satisfactory performance on segmenting leaves from the background as a
whole and counting the number of leaves using simple data augmentation
strategies. Comparative analysis is provided against methods evaluated on the
previous competition datasets. Our framework achieves mean and standard
deviation of absolute count difference of 1.62 and 2.30 averaged over all five
test datasets.
An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs
Authors: Jun Chen, Qiang Yao, Houari Sabirin, Keisuke Nonaka, Hiroshi Sankoh, Sei Naito
Date: 2017-08-28 00:00:00
Tasks: GPU

In this paper, we report an optimized union-find (UF) algorithm that can
label the connected components on a 2D image efficiently by employing the GPU
architecture. The proposed method contains three phases: UF-based local merge,
boundary analysis, and link. The coarse labeling in local merge reduces the
number atomic operations, while the boundary analysis only manages the pixels
on the boundary of each block. Evaluation results showed that the proposed
algorithm speed up the average running time by more than 1.3X.
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Authors: Jia Ding, Aoxue Li, Zhiqiang Hu, Li-Wei Wang
Date: 2017-06-14 00:00:00
Tasks: Computed Tomography (CT)

Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).
Universal Semantic Parsing
Authors: Siva Reddy, Oscar Täckström, Slav Petrov, Mark Steedman, Mirella Lapata
Date: 2017-02-10 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering, Semantic Parsing

Universal Dependencies (UD) offer a uniform cross-lingual syntactic
representation, with the aim of advancing multilingual applications. Recent
work shows that semantic parsing can be accomplished by transforming syntactic
dependencies to logical forms. However, this work is limited to English, and
cannot process dependency graphs, which allow handling complex phenomena such
as control. In this work, we introduce UDepLambda, a semantic interface for UD,
which maps natural language to logical forms in an almost language-independent
fashion and can process dependency graphs. We perform experiments on question
answering against Freebase and provide German and Spanish translations of the
WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation.
Results show that UDepLambda outperforms strong baselines across languages and
datasets. For English, it achieves a 4.9 F1 point improvement over the
state-of-the-art on GraphQuestions. Our code and data can be downloaded at
https://github.com/sivareddyg/udeplambda.
An inexact subsampled proximal Newton-type method for large-scale machine learning
Authors: Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun
Date: 2017-08-28 00:00:00
Tasks: BIG-bench Machine Learning

We propose a fast proximal Newton-type algorithm for minimizing regularized
finite sums that returns an $\epsilon$-suboptimal point in
$\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS,
where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the
condition number. As long as $n > d$, the proposed method is more efficient
than state-of-the-art accelerated stochastic first-order methods for non-smooth
regularizers which requires $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa
n})\log(\frac{1}{\epsilon}))$ FLOPS. The key idea is to form the subsampled
Newton subproblem in a way that preserves the finite sum structure of the
objective, thereby allowing us to leverage recent developments in stochastic
first-order methods to solve the subproblem. Experimental results verify that
the proposed algorithm outperforms previous algorithms for $\ell_1$-regularized
logistic regression on real datasets.
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks
Authors: Mohammad Amin Nabian, Hadi Meidani
Date: 2017-08-28 00:00:00
Tasks: Deep Learning

Natural disasters can have catastrophic impacts on the functionality of
infrastructure systems and cause severe physical and socio-economic losses.
Given budget constraints, it is crucial to optimize decisions regarding
mitigation, preparedness, response, and recovery practices for these systems.
This requires accurate and efficient means to evaluate the infrastructure
system reliability. While numerous research efforts have addressed and
quantified the impact of natural disasters on infrastructure systems, typically
using the Monte Carlo approach, they still suffer from high computational cost
and, thus, are of limited applicability to large systems. This paper presents a
deep learning framework for accelerating infrastructure system reliability
analysis. In particular, two distinct deep neural network surrogates are
constructed and studied: (1) A classifier surrogate which speeds up the
connectivity determination of networks, and (2) An end-to-end surrogate that
replaces a number of components such as roadway status realization,
connectivity determination, and connectivity averaging. The proposed approach
is applied to a simulation-based study of the two-terminal connectivity of a
California transportation network subject to extreme probabilistic earthquake
events. Numerical results highlight the effectiveness of the proposed approach
in accelerating the transportation system two-terminal reliability analysis
with extremely high prediction accuracy.
VQABQ: Visual Question Answering by Basic Questions
Authors: Jia-Hong Huang, Modar Alfadly, Bernard Ghanem
Date: 2017-03-19 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Taking an image and question as the input of our method, it can output the
text-based answer of the query question about the given image, so called Visual
Question Answering (VQA). There are two main modules in our algorithm. Given a
natural language question about an image, the first module takes the question
as input and then outputs the basic questions of the main given question. The
second module takes the main question, image and these basic questions as input
and then outputs the text-based answer of the main question. We formulate the
basic questions generation problem as a LASSO optimization problem, and also
propose a criterion about how to exploit these basic questions to help answer
main question. Our method is evaluated on the challenging VQA dataset and
yields state-of-the-art accuracy, 60.34% in open-ended task.
Investigating Reinforcement Learning Agents for Continuous State Space Environments
Authors: David Von Dollen
Date: 2017-08-08 00:00:00
Tasks: OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Given an environment with continuous state spaces and discrete actions, we
investigate using a Double Deep Q-learning Reinforcement Agent to find optimal
policies using the LunarLander-v2 OpenAI gym environment.
THAP: A Matlab Toolkit for Learning with Hawkes Processes
Authors: Hongteng Xu, Hongyuan Zha
Date: 2017-08-28 00:00:00
Tasks: Point Processes

As a powerful tool of asynchronous event sequence analysis, point processes
have been studied for a long time and achieved numerous successes in different
fields. Among various point process models, Hawkes process and its variants
attract many researchers in statistics and computer science these years because
they capture the self- and mutually-triggering patterns between different
events in complicated sequences explicitly and quantitatively and are broadly
applicable to many practical problems. In this paper, we describe an
open-source toolkit implementing many learning algorithms and analysis tools
for Hawkes process model and its variants. Our toolkit systematically
summarizes recent state-of-the-art algorithms as well as most classic
algorithms of Hawkes processes, which is beneficial for both academical
education and research. Source code can be downloaded from
https://github.com/HongtengXu/Hawkes-Process-Toolkit.
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
Authors: Kai Zhao, Liang Huang
Date: 2017-08-28 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing, Discourse Parsing

Discourse parsing has long been treated as a stand-alone problem independent
from constituency or dependency parsing. Most attempts at this problem are
pipelined rather than end-to-end, sophisticated, and not self-contained: they
assume gold-standard text segmentations (Elementary Discourse Units), and use
external parsers for syntactic features. In this paper we propose the first
end-to-end discourse parser that jointly parses in both syntax and discourse
levels, as well as the first syntacto-discourse treebank by integrating the
Penn Treebank with the RST Treebank. Built upon our recent span-based
constituency parser, this joint syntacto-discourse parser requires no
preprocessing whatsoever (such as segmentation or feature extraction), achieves
the state-of-the-art end-to-end discourse parsing accuracy.
Stem-ming the Tide: Predicting STEM attrition using student transcript data
Authors: Lovenoor Aulck, Rohan Aras, Lysia Li, Coulter L'Heureux, Peter Lu, Jevin West
Date: 2017-08-28 00:00:00
Tasks: BIG-bench Machine Learning, Math

Science, technology, engineering, and math (STEM) fields play growing roles
in national and international economies by driving innovation and generating
high salary jobs. Yet, the US is lagging behind other highly industrialized
nations in terms of STEM education and training. Furthermore, many economic
forecasts predict a rising shortage of domestic STEM-trained professions in the
US for years to come. One potential solution to this deficit is to decrease the
rates at which students leave STEM-related fields in higher education, as
currently over half of all students intending to graduate with a STEM degree
eventually attrite. However, little quantitative research at scale has looked
at causes of STEM attrition, let alone the use of machine learning to examine
how well this phenomenon can be predicted. In this paper, we detail our efforts
to model and predict dropout from STEM fields using one of the largest known
datasets used for research on students at a traditional campus setting. Our
results suggest that attrition from STEM fields can be accurately predicted
with data that is routinely collected at universities using only information on
students' first academic year. We also propose a method to model student STEM
intentions for each academic term to better understand the timing of STEM
attrition events. We believe these results show great promise in using machine
learning to improve STEM retention in traditional and non-traditional campus
settings.
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection
Authors: JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates
Date: 2017-08-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Seizure Detection

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming
a reality with the increasing availability of small, diverse, robust, high
fidelity sensors. This oncoming flood of data begs the question of how we will
extract useful information from it. In this paper we explore the use of a
variety of representations and machine learning algorithms applied to the task
of seizure detection in high resolution, multichannel EEG data. We explore
classification accuracy, computational complexity and memory requirements with
a view toward understanding which approaches are most suitable for such tasks
as the number of people involved and the amount of data they produce grows to
be quite large. In particular, we show that layered learning approaches such as
Deep Belief Networks excel along these dimensions.
Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild
Authors: Christopher Funk, Yanxi Liu
Date: 2017-04-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Symmetry Detection

Humans take advantage of real world symmetries for various tasks, yet
capturing their superb symmetry perception mechanism with a computational model
remains elusive. Motivated by a new study demonstrating the extremely high
inter-person accuracy of human perceived symmetries in the wild, we have
constructed the first deep-learning neural network for reflection and rotation
symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common
Object in COntext) dataset with nearly 11K consistent symmetry-labels from more
than 400 human observers. We employ novel methods to convert discrete human
labels into symmetry heatmaps, capture symmetry densely in an image and
quantitatively evaluate Sym-NET against multiple existing computer vision
algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO
photos, Sym-NET significantly outperforms all other competitors. Beyond
mathematically well-defined symmetries on a plane, Sym-NET demonstrates
abilities to identify viewpoint-varied 3D symmetries, partially occluded
symmetrical objects, and symmetries at a semantic level.
Folding membrane proteins by deep transfer learning
Authors: Sheng Wang, Zhen Li, Yizhou Yu, Jinbo Xu
Date: 2017-08-28 00:00:00
Tasks: Transfer Learning

Computational elucidation of membrane protein (MP) structures is challenging
partially due to lack of sufficient solved structures for homology modeling.
Here we describe a high-throughput deep transfer learning method that first
predicts MP contacts by learning from non-membrane proteins (non-MPs) and then
predicting three-dimensional structure models using the predicted contacts as
distance restraints. Tested on 510 non-redundant MPs, our method has contact
prediction accuracy at least 0.18 better than existing methods, predicts
correct folds for 218 MPs (TMscore at least 0.6), and generates
three-dimensional models with RMSD less than 4 Angstrom and 5 Angstrom for 57
and 108 MPs, respectively. A rigorous blind test in the continuous automated
model evaluation (CAMEO) project shows that our method predicted
high-resolution three-dimensional models for two recent test MPs of 210
residues with RMSD close to 2 Angstrom. We estimated that our method could
predict correct folds for between 1,345 and 1,871 reviewed human multi-pass MPs
including a few hundred new folds, which shall facilitate the discovery of
drugs targeting at membrane proteins.
A Bayesian algorithm for distributed network localization using distance and direction data
Authors: Hassan Naseri, Visa Koivunen
Date: 2017-04-06 00:00:00

A reliable, accurate, and affordable positioning service is highly required
in wireless networks. In this paper, the novel Message Passing Hybrid
Localization (MPHL) algorithm is proposed to solve the problem of cooperative
distributed localization using distance and direction estimates. This hybrid
approach combines two sensing modalities to reduce the uncertainty in
localizing the network nodes. A statistical model is formulated for the
problem, and approximate minimum mean square error (MMSE) estimates of the node
locations are computed. The proposed MPHL is a distributed algorithm based on
belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
improves the identifiability of the localization problem and reduces its
sensitivity to the anchor node geometry, compared to distance-only or
direction-only localization techniques. For example, the unknown location of a
node can be found if it has only a single neighbor; and a whole network can be
localized using only a single anchor node. Numerical results are presented
showing that the average localization error is significantly reduced in almost
every simulation scenario, about 50% in most cases, compared to the competing
algorithms.
The Tensor Memory Hypothesis
Authors: Volker Tresp, Yunpu Ma
Date: 2017-08-09 00:00:00

We discuss memory models which are based on tensor decompositions using
latent representations of entities and events. We show how episodic memory and
semantic memory can be realized and discuss how new memory traces can be
generated from sensory input: Existing memories are the basis for perception
and new memories are generated via perception. We relate our mathematical
approach to the hippocampal memory indexing theory. We describe the first
detailed mathematical models for the complete processing pipeline from sensory
input and its semantic decoding, i.e., perception, to the formation of episodic
and semantic memories and their declarative semantic decodings. Our main
hypothesis is that perception includes an active semantic decoding process,
which relies on latent representations of entities and predicates, and that
episodic and semantic memories depend on the same decoding process. We
contribute to the debate between the leading memory consolidation theories,
i.e., the standard consolidation theory (SCT) and the multiple trace theory
(MTT). The latter is closely related to the complementary learning systems
(CLS) framework. In particular, we show explicitly how episodic memory can
teach the neocortex to form a semantic memory, which is a core issue in MTT and
CLS.