pwc-archive/papers-with-abstracts huggingface dataset:
A Deterministic Algorithm for Bridging Anaphora Resolution
Authors: Yufang Hou
Date: 2018-11-14 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Bridging Anaphora Resolution, Word Embeddings

Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et
al., 2013b) use syntactic preposition patterns to calculate word relatedness.
However, such patterns only consider NPs' head nouns and hence do not fully
capture the semantics of NPs. Recently, Hou (2018) created word embeddings
(embeddings_PP) to capture associative similarity (ie, relatedness) between
nouns by exploring the syntactic structure of noun phrases. But embeddings_PP
only contains word representations for nouns. In this paper, we create new word
vectors by combining embeddings_PP with GloVe. This new word embeddings
(embeddings_bridging) are a more general lexical knowledge resource for
bridging and allow us to represent the meaning of an NP beyond its head easily.
We therefore develop a deterministic approach for bridging anaphora resolution,
which represents the semantics of an NP based on its head noun and
modifications. We show that this simple approach achieves the competitive
results compared to the best system in Hou et al.(2013b) which explores Markov
Logic Networks to model the problem. Additionally, we further improve the
results for bridging anaphora resolution reported in Hou (2018) by combining
our simple deterministic approach with Hou et al.(2013b)'s best system MLN II.
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
Authors: M. Tarik Altuncu, Erik Mayer, Sophia N. Yaliraki, Mauricio Barahona
Date: 2018-11-14 00:00:00
Tasks: Community Detection, Descriptive, graph partitioning

Electronic Healthcare records contain large volumes of unstructured data in
different forms. Free text constitutes a large portion of such data, yet this
source of richly detailed information often remains under-used in practice
because of a lack of suitable methodologies to extract interpretable content in
a timely manner. Here we apply network-theoretical tools to the analysis of
free text in Hospital Patient Incident reports in the English National Health
Service, to find clusters of reports in an unsupervised manner and at different
levels of resolution based directly on the free text descriptions contained
within them. To do so, we combine recently developed deep neural network
text-embedding methodologies based on paragraph vectors with multi-scale Markov
Stability community detection applied to a similarity graph of documents
obtained from sparsified text vector similarities. We showcase the approach
with the analysis of incident reports submitted in Imperial College Healthcare
NHS Trust, London. The multiscale community structure reveals levels of meaning
with different resolution in the topics of the dataset, as shown by relevant
descriptive terms extracted from the groups of records, as well as by comparing
a posteriori against hand-coded categories assigned by healthcare personnel.
Our content communities exhibit good correspondence with well-defined
hand-coded categories, yet our results also provide further medical detail in
certain areas as well as revealing complementary descriptors of incidents
beyond the external classification. We also discuss how the method can be used
to monitor reports over time and across different healthcare providers, and to
detect emerging trends that fall outside of pre-existing categories.
Plan-And-Write: Towards Better Automatic Storytelling
Authors: Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan
Date: 2018-11-14 00:00:00
Tasks: Story Generation

Automatic storytelling is challenging since it requires generating long,
coherent natural language to describes a sensible sequence of events. Despite
considerable efforts on automatic story generation in the past, prior work
either is restricted in plot planning, or can only generate stories in a narrow
domain. In this paper, we explore open-domain story generation that writes
stories given a title (topic) as input. We propose a plan-and-write
hierarchical generation framework that first plans a storyline, and then
generates a story based on the storyline. We compare two planning strategies.
The dynamic schema interweaves story planning and its surface realization in
text, while the static schema plans out the entire storyline before generating
stories. Experiments show that with explicit storyline planning, the generated
stories are more diverse, coherent, and on topic than those generated without
creating a full plan, according to both automatic and human evaluations.
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Authors: Ezequiel de la Rosa, Diana M. Sima, Thijs Vande Vyvere, Jan S. Kirschke, Bjoern Menze
Date: 2018-11-14 00:00:00
Tasks: Decision Making, Injury Prediction, Lesion Classification, Lesion Detection

Computer Tomography (CT) is the gold standard technique for brain damage
evaluation after acute Traumatic Brain Injury (TBI). It allows identification
of most lesion types and determines the need of surgical or alternative
therapeutic procedures. However, the traditional approach for lesion
classification is restricted to visual image inspection. In this work, we
characterize and predict TBI lesions by using CT-derived radiomics descriptors.
Relevant shape, intensity and texture biomarkers characterizing the different
lesions are isolated and a lesion predictive model is built by using Partial
Least Squares. On a dataset containing 155 scans (105 train, 50 test) the
methodology achieved 89.7 % accuracy over the unseen data. When a model was
build using only texture features, a 88.2 % accuracy was obtained. Our results
suggest that selected radiomics descriptors could play a key role in brain
injury prediction. Besides, the proposed methodology is close to reproduce
radiologists decision making. These results open new possibilities for
radiomics-inspired brain lesion detection, segmentation and prediction.
Generating Multiple Diverse Responses for Short-Text Conversation
Authors: Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Shuming Shi
Date: 2018-11-14 00:00:00
Tasks: Diversity, Informativeness, Reinforcement Learning, Response Generation, Short-Text Conversation

Neural generative models have become popular and achieved promising
performance on short-text conversation tasks. They are generally trained to
build a 1-to-1 mapping from the input post to its output response. However, a
given post is often associated with multiple replies simultaneously in real
applications. Previous research on this task mainly focuses on improving the
relevance and informativeness of the top one generated response for each post.
Very few works study generating multiple accurate and diverse responses for the
same post. In this paper, we propose a novel response generation model, which
considers a set of responses jointly and generates multiple diverse responses
simultaneously. A reinforcement learning algorithm is designed to solve our
model. Experiments on two short-text conversation tasks validate that the
multiple responses generated by our model obtain higher quality and larger
diversity compared with various state-of-the-art generative models.
Efficient and Scalable Multi-task Regression on Massive Number of Tasks
Authors: Xiao He, Francesco Alesiani, Ammar Shaker
Date: 2018-11-14 00:00:00
Tasks: Clustering, Computational Efficiency, Multi-Task Learning, regression

Many real-world large-scale regression problems can be formulated as
Multi-task Learning (MTL) problems with a massive number of tasks, as in retail
and transportation domains. However, existing MTL methods still fail to offer
both the generalization performance and the scalability for such problems.
Scaling up MTL methods to problems with a tremendous number of tasks is a big
challenge. Here, we propose a novel algorithm, named Convex Clustering
Multi-Task regression Learning (CCMTL), which integrates with convex clustering
on the k-nearest neighbor graph of the prediction models. Further, CCMTL
efficiently solves the underlying convex problem with a newly proposed
optimization method. CCMTL is accurate, efficient to train, and empirically
scales linearly in the number of tasks. On both synthetic and real-world
datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA)
multi-task learning methods in terms of prediction accuracy as well as
computational efficiency. On a real-world retail dataset with 23,812 tasks,
CCMTL requires only around 30 seconds to train on a single thread, while the
SoA methods need up to hours or even days.
Leveraging Aspect Phrase Embeddings for Cross-Domain Review Rating Prediction
Authors: Aiqi Jiang, Arkaitz Zubiaga
Date: 2018-11-14 00:00:00
Tasks: Prediction

Online review platforms are a popular way for users to post reviews by
expressing their opinions towards a product or service, as well as they are
valuable for other users and companies to find out the overall opinions of
customers. These reviews tend to be accompanied by a rating, where the star
rating has become the most common approach for users to give their feedback in
a quantitative way, generally as a likert scale of 1-5 stars. In other social
media platforms like Facebook or Twitter, an automated review rating prediction
system can be useful to determine the rating that a user would have given to
the product or service. Existing work on review rating prediction focuses on
specific domains, such as restaurants or hotels. This, however, ignores the
fact that some review domains which are less frequently rated, such as
dentists, lack sufficient data to build a reliable prediction model. In this
paper, we experiment on 12 datasets pertaining to 12 different review domains
of varying level of popularity to assess the performance of predictions across
different domains. We introduce a model that leverages aspect phrase embeddings
extracted from the reviews, which enables the development of both in-domain and
cross-domain review rating prediction systems. Our experiments show that both
of our review rating prediction systems outperform all other baselines. The
cross-domain review rating prediction system is particularly significant for
the least popular review domains, where leveraging training data from other
domains leads to remarkable improvements in performance. The in-domain review
rating prediction system is instead more suitable for popular review domains,
provided that a model built from training data pertaining to the target domain
is more suitable when this data is abundant.
Melodic Phrase Segmentation By Deep Neural Networks
Authors: Yixing Guan, Jinyu Zhao, Yiqin Qiu, Zheng Zhang, Gus Xia
Date: 2018-11-14 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval, Segmentation

Automated melodic phrase detection and segmentation is a classical task in
content-based music information retrieval and also the key towards automated
music structure analysis. However, traditional methods still cannot satisfy
practical requirements. In this paper, we explore and adapt various neural
network architectures to see if they can be generalized to work with the
symbolic representation of music and produce satisfactory melodic phrase
segmentation. The main issue of applying deep-learning methods to phrase
detection is the sparse labeling problem of training sets. We proposed two
tailored label engineering with corresponding training techniques for different
neural networks in order to make decisions at a sequential level. Experiment
results show that the CNN-CRF architecture performs the best, being able to
offer finer segmentation and faster to train, while CNN, Bi-LSTM-CNN and
Bi-LSTM-CRF are acceptable alternatives.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
Authors: Haifeng Zhang, Zilong Guo, Han Cai, Chris Wang, Wei-Nan Zhang, Yong Yu, Wenxin Li, Jun Wang
Date: 2018-11-14 00:00:00
Tasks: Layout Design

With the rapid growth of the express industry, intelligent warehouses that
employ autonomous robots for carrying parcels have been widely used to handle
the vast express volume. For such warehouses, the warehouse layout design plays
a key role in improving the transportation efficiency. However, this work is
still done by human experts, which is expensive and leads to suboptimal
results. In this paper, we aim to automate the warehouse layout designing
process. We propose a two-layer evolutionary algorithm to efficiently explore
the warehouse layout space, where an auxiliary objective fitness approximation
model is introduced to predict the outcome of the designed warehouse layout and
a two-layer population structure is proposed to incorporate the approximation
model into the ordinary evolution framework. Empirical experiments show that
our method can efficiently design effective warehouse layouts that outperform
both heuristic-designed and vanilla evolution-designed warehouse layouts.
Modeling Coherence for Discourse Neural Machine Translation
Authors: Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang
Date: 2018-11-14 00:00:00
Tasks: Machine Translation, Sentence, Translation

Discourse coherence plays an important role in the translation of one text.
However, the previous reported models most focus on improving performance over
individual sentence while ignoring cross-sentence links and dependencies, which
affects the coherence of the text. In this paper, we propose to use discourse
context and reward to refine the translation quality from the discourse
perspective. In particular, we generate the translation of individual sentences
at first. Next, we deliberate the preliminary produced translations, and train
the model to learn the policy that produces discourse coherent text by a reward
teacher. Practical results on multiple discourse test datasets indicate that
our model significantly improves the translation quality over the
state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model
generates more discourse coherent text and obtains +2.2 BLEU improvements when
evaluated by discourse metrics.
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Authors: Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar
Date: 2018-11-14 00:00:00
Tasks: Band Gap, BIG-bench Machine Learning, Formation Energy, Multi-Task Learning, Property Prediction, Transfer Learning

Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of universal descriptor of materials to predict its various properties.
The limited availability of materials data can be addressed through transfer
learning, while the generic representation was recently addressed by Xie and
Grossman [1], where they developed a crystal graph convolutional neural network
(CGCNN) that provides a unified representation of crystals. In this work, we
develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning
based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN
by simultaneous prediction of various material properties such as Formation
Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774
materials). MT-CGCNN is able to reduce the test error when employed on
correlated properties by upto 8%. The model prediction has lower test error
compared to CGCNN, even when the training data is reduced by 10%. We also
demonstrate our model's better performance through prediction of end user
scenario related to metal/non-metal classification. These results encourage
further development of machine learning approaches which leverage multi-task
learning to address the aforementioned challenges in the discovery of new
materials. We make MT-CGCNN's source code available to encourage reproducible
research.
Sample complexity of partition identification using multi-armed bandits
Authors: Sandeep Juneja, Subhashini Krishnasamy
Date: 2018-11-14 00:00:00
Tasks: Multi-Armed Bandits

Given a vector of probability distributions, or arms, each of which can be
sampled independently, we consider the problem of identifying the partition to
which this vector belongs from a finitely partitioned universe of such vector
of distributions. We study this as a pure exploration problem in multi armed
bandit settings and develop sample complexity bounds on the total mean number
of samples required for identifying the correct partition with high
probability. This framework subsumes well studied problems such as finding the
best arm or the best few arms. We consider distributions belonging to the
single parameter exponential family and primarily consider partitions where the
vector of means of arms lie either in a given set or its complement. The sets
considered correspond to distributions where there exists a mean above a
specified threshold, where the set is a half space and where either the set or
its complement is a polytope, or more generally, a convex set. In these
settings, we characterize the lower bounds on mean number of samples for each
arm highlighting their dependence on the problem geometry. Further, inspired by
the lower bounds, we propose algorithms that can match these bounds
asymptotically with decreasing probability of error. Applications of this
framework may be diverse. We briefly discuss one associated with finance.
Fast Distribution Grid Line Outage Identification with $μ$PMU
Authors: Yizheng Liao, Yang Weng, Chin-Woo Tan, Ram Rajagopal
Date: 2018-11-14 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

The growing integration of distributed energy resources (DERs) in urban
distribution grids raises various reliability issues due to DER's uncertain and
complex behaviors. With a large-scale DER penetration, traditional outage
detection methods, which rely on customers making phone calls and smart meters'
"last gasp" signals, will have limited performance, because the renewable
generators can supply powers after line outages and many urban grids are mesh
so line outages do not affect power supply. To address these drawbacks, we
propose a data-driven outage monitoring approach based on the stochastic time
series analysis from micro phasor measurement unit ($\mu$PMU). Specifically, we
prove via power flow analysis that the dependency of time-series voltage
measurements exhibits significant statistical changes after line outages. This
makes the theory on optimal change-point detection suitable to identify line
outages via $\mu$PMUs with fast and accurate sampling. However, existing change
point detection methods require post-outage voltage distribution unknown in
distribution systems. Therefore, we design a maximum likelihood-based method to
directly learn the distribution parameters from $\mu$PMU data. We prove that
the estimated parameters-based detection still achieves the optimal
performance, making it extremely useful for distribution grid outage
identifications. Simulation results show highly accurate outage identification
in eight distribution grids with 14 configurations with and without DERs using
$\mu$PMU data.
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
Authors: Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li
Date: 2018-11-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, Image Clustering

Symmetric nonnegative matrix factorization (NMF), a special but important
class of the general NMF, is demonstrated to be useful for data analysis and in
particular for various clustering tasks. Unfortunately, designing fast
algorithms for Symmetric NMF is not as easy as for the nonsymmetric
counterpart, the latter admitting the splitting property that allows efficient
alternating-type algorithms. To overcome this issue, we transfer the symmetric
NMF to a nonsymmetric one, then we can adopt the idea from the state-of-the-art
algorithms for nonsymmetric NMF to design fast algorithms solving symmetric
NMF. We rigorously establish that solving nonsymmetric reformulation returns a
solution for symmetric NMF and then apply fast alternating based algorithms for
the corresponding reformulated problem. Furthermore, we show these fast
algorithms admit strong convergence guarantee in the sense that the generated
sequence is convergent at least at a sublinear rate and it converges globally
to a critical point of the symmetric NMF. We conduct experiments on both
synthetic data and image clustering to support our result.
Translating a Math Word Problem to an Expression Tree
Authors: Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, Xiaojiang Liu
Date: 2018-11-14 00:00:00
Tasks: Math, Math Word Problem Solving

Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to
automatic math word problem solving. Despite its simplicity, a drawback still
remains: a math word problem can be correctly solved by more than one
equations. This non-deterministic transduction harms the performance of maximum
likelihood estimation. In this paper, by considering the uniqueness of
expression tree, we propose an equation normalization method to normalize the
duplicated equations. Moreover, we analyze the performance of three popular
SEQ2SEQ models on the math word problem solving. We find that each model has
its own specialty in solving problems, consequently an ensemble model is then
proposed to combine their advantages. Experiments on dataset Math23K show that
the ensemble model with equation normalization significantly outperforms the
previous state-of-the-art methods.
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction
Authors: Kui Fu, Jia Li, Yu Zhang, Hongze Shen, Yonghong Tian
Date: 2018-11-14 00:00:00
Tasks: Aerial Video Saliency Prediction, Prediction, Saliency Prediction, Transfer Learning, Video Saliency Prediction

As an emerging vision platform, a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction. To investigate these challenges, this paper proposes a large-scale video dataset for aerial saliency prediction, which consists of ground-truth salient object regions of 1,000 aerial videos, annotated by 24 subjects. To the best of our knowledge, it is the first large-scale video dataset that focuses on visual saliency prediction on drones. Based on this dataset, we propose a Model-guided Multi-path Network (MM-Net) that serves as a baseline model for aerial video saliency prediction. Inspired by the annotation process in eye-tracking experiments, MM-Net adopts multiple information paths, each of which is initialized under the guidance of a classic saliency model. After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios. Finally, these spatial predictions are adaptively combined with the temporal saliency predictions via a spatiotemporal optimization algorithm. Experimental results show that MM-Net outperforms ten state-of-the-art models in predicting aerial video saliency.