pwc-archive/papers-with-abstracts huggingface dataset:
Convergence Rate of Krasulina Estimator
Authors: Jiangning Chen
Date: 2018-08-28 00:00:00

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. Consider the points $X_1, X_2,..., X_n$ are vectors drawn i.i.d. from a distribution with mean zero and covariance $\Sigma$, where $\Sigma$ is unknown. Let $A_n = X_nX_n^T$, then $E[A_n] = \Sigma$. This paper consider the problem of finding the least eigenvalue and eigenvector of matrix $\Sigma$. A classical such estimator are due to Krasulina\cite{krasulina_method_1969}. We are going to state the convergence proof of Krasulina for the least eigenvalue and corresponding eigenvector, and then find their convergence rate.
Residualized Factor Adaptation for Community Social Media Prediction Tasks
Authors: Mohammadzaman Zamani, H. Andrew Schwartz, Veronica E. Lynn, Salvatore Giorgi, Niranjan Balasubramanian
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Prediction

Predictive models over social media language have shown promise in capturing
community outcomes, but approaches thus far largely neglect the
socio-demographic context (e.g. age, education rates, race) of the community
from which the language originates. For example, it may be inaccurate to assume
people in Mobile, Alabama, where the population is relatively older, will use
words the same way as those from San Francisco, where the median age is younger
with a higher rate of college education. In this paper, we present residualized
factor adaptation, a novel approach to community prediction tasks which both
(a) effectively integrates community attributes, as well as (b) adapts
linguistic features to community attributes (factors). We use eleven
demographic and socioeconomic attributes, and evaluate our approach over five
different community-level predictive tasks, spanning health (heart disease
mortality, percent fair/poor health), psychology (life satisfaction), and
economics (percent housing price increase, foreclosure rate). Our evaluation
shows that residualized factor adaptation significantly improves 4 out of 5
community-level outcome predictions over prior state-of-the-art for
incorporating socio-demographic contexts.
Learning To Split and Rephrase From Wikipedia Edit History
Authors: Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Split and Rephrase

Split and rephrase is the task of breaking down a sentence into shorter ones
that together convey the same meaning. We extract a rich new dataset for this
task by mining Wikipedia's edit history: WikiSplit contains one million
naturally occurring sentence rewrites, providing sixty times more distinct
split examples and a ninety times larger vocabulary than the WebSplit corpus
introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating
WikiSplit as training data produces a model with qualitatively better
predictions that score 32 BLEU points above the prior best result on the
WebSplit benchmark.
The University of Cambridge's Machine Translation Systems for WMT18
Authors: Felix Stahlberg, Adria de Gispert, Bill Byrne
Date: 2018-08-28 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

The University of Cambridge submission to the WMT18 news translation task
focuses on the combination of diverse models of translation. We compare
recurrent, convolutional, and self-attention-based neural models on
German-English, English-German, and Chinese-English. Our final system combines
all neural models together with a phrase-based SMT system in an MBR-based
scheme. We report small but consistent gains on top of strong Transformer
ensembles.
Graphene: A Context-Preserving Open Information Extraction System
Authors: Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh
Date: 2018-08-28 00:00:00
Proceeding: COLING 2018 8
Tasks: Open Information Extraction, Sentence

We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.
Contextual Audio-Visual Switching For Speech Enhancement in Real-World Environments
Authors: Ahsan Adeel, Mandar Gogate, Amir Hussain
Date: 2018-08-28 00:00:00
Tasks: Lip Reading, Speech Enhancement

Human speech processing is inherently multimodal, where visual cues (lip
movements) help to better understand the speech in noise. Lip-reading driven
speech enhancement significantly outperforms benchmark audio-only approaches at
low signal-to-noise ratios (SNRs). However, at high SNRs or low levels of
background noise, visual cues become fairly less effective for speech
enhancement. Therefore, a more optimal, context-aware audio-visual (AV) system
is required, that contextually utilises both visual and noisy audio features
and effectively accounts for different noisy conditions. In this paper, we
introduce a novel contextual AV switching component that contextually exploits
AV cues with respect to different operating conditions to estimate clean audio,
without requiring any SNR estimation. The switching module switches between
visual-only (V-only), audio-only (A-only), and both AV cues at low, high and
moderate SNR levels, respectively. The contextual AV switching component is
developed by integrating a convolutional neural network and long-short-term
memory network. For testing, the estimated clean audio features are utilised by
the developed novel enhanced visually derived Wiener filter for clean audio
power spectrum estimation. The contextual AV speech enhancement method is
evaluated under real-world scenarios using benchmark Grid and ChiME3 corpora.
For objective testing, perceptual evaluation of speech quality is used to
evaluate the quality of the restored speech. For subjective testing, the
standard mean-opinion-score method is used. The critical analysis and
comparative study demonstrate the outperformance of proposed contextual AV
approach, over A-only, V-only, spectral subtraction, and log-minimum mean
square error based speech enhancement methods at both low and high SNRs,
revealing its capability to tackle spectro-temporal variation in any real-world
noisy condition.
An Efficient Matheuristic for the Minimum-Weight Dominating Set Problem
Authors: Mayra Albuquerque, Thibaut Vidal
Date: 2018-08-28 00:00:00

A minimum dominating set in a graph is a minimum set of vertices such that
every vertex of the graph either belongs to it, or is adjacent to one vertex of
this set. This mathematical object is of high relevance in a number of
applications related to social networks analysis, design of wireless networks,
coding theory, and data mining, among many others. When vertex weights are
given, minimizing the total weight of the dominating set gives rise to a
problem variant known as the minimum weight dominating set problem. To solve
this problem, we introduce a hybrid matheuristic combining a tabu search with
an integer programming solver. The latter is used to solve subproblems in which
only a fraction of the decision variables, selected relatively to the search
history, are left free while the others are fixed. Moreover, we introduce an
adaptive penalty to promote the exploration of intermediate infeasible
solutions during the search, enhance the algorithm with perturbations and node
elimination procedures, and exploit richer neighborhood classes. Extensive
experimental analyses on a variety of instance classes demonstrate the good
performance of the algorithm, and the contribution of each component in the
success of the search is analyzed.
Using Taste Groups for Collaborative Filtering
Authors: Farhan Khawar, Nevin L. Zhang
Date: 2018-08-28 00:00:00
Tasks: Collaborative Filtering

Implicit feedback is the simplest form of user feedback that can be used for
item recommendation. It is easy to collect and domain independent. However,
there is a lack of negative examples. Existing works circumvent this problem by
making various assumptions regarding the unconsumed items, which fail to hold
when the user did not consume an item because she was unaware of it. In this
paper, we propose as a novel method for addressing the lack of negative
examples in implicit feedback. The motivation is that if there is a large group
of users who share the same taste and none of them consumed an item, then it is
highly likely that the item is irrelevant to this taste. We use Hierarchical
Latent Tree Analysis(HLTA) to identify taste-based user groups and make
recommendations for a user based on her memberships in the groups.
Iterative Deep Learning for Road Topology Extraction
Authors: Carles Ventura, Jordi Pont-Tuset, Sergi Caelles, Kevis-Kokitsi Maninis, Luc van Gool
Date: 2018-08-28 00:00:00
Tasks: Deep Learning, General Classification

This paper tackles the task of estimating the topology of road networks from
aerial images. Building on top of a global model that performs a dense
semantical classification of the pixels of the image, we design a Convolutional
Neural Network (CNN) that predicts the local connectivity among the central
pixel of an input patch and its border points. By iterating this local
connectivity we sweep the whole image and infer the global topology of the road
network, inspired by a human delineating a complex network with the tip of
their finger. We perform an extensive and comprehensive qualitative and
quantitative evaluation on the road network estimation task, and show that our
method also generalizes well when moving to networks of retinal vessels.
A Quantum Many-body Wave Function Inspired Language Modeling Approach
Authors: Peng Zhang, Zhan Su, Lipeng Zhang, Benyou Wang, Dawei Song
Date: 2018-08-28 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, Sentence, Word Embeddings

The recently proposed quantum language model (QLM) aimed at a principled
approach to modeling term dependency by applying the quantum probability
theory. The latest development for a more effective QLM has adopted word
embeddings as a kind of global dependency information and integrated the
quantum-inspired idea in a neural network architecture. While these
quantum-inspired LMs are theoretically more general and also practically
effective, they have two major limitations. First, they have not taken into
account the interaction among words with multiple meanings, which is common and
important in understanding natural language text. Second, the integration of
the quantum-inspired LM with the neural network was mainly for effective
training of parameters, yet lacking a theoretical foundation accounting for
such integration. To address these two issues, in this paper, we propose a
Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The
QMWF inspired LM can adopt the tensor product to model the aforesaid
interaction among words. It also enables us to reveal the inherent necessity of
using Convolutional Neural Network (CNN) in QMWF language modeling.
Furthermore, our approach delivers a simple algorithm to represent and match
text/sentence pairs. Systematic evaluation shows the effectiveness of the
proposed QMWF-LM algorithm, in comparison with the state of the art
quantum-inspired LMs and a couple of CNN-based methods, on three typical
Question Answering (QA) datasets.
Superhighway: Bypass Data Sparsity in Cross-Domain CF
Authors: Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi, Yi-An Chen, Ming-Feng Tsai, Chuan-Ju Wang
Date: 2018-08-28 00:00:00
Tasks: Collaborative Filtering

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in
single-domain CF by leveraging knowledge transferred from related domains. Many
traditional methods focus on enriching compared neighborhood relations in CF
directly to address the sparsity problem. In this paper, we propose
superhighway construction, an alternative explicit relation-enrichment
procedure, to improve recommendations by enhancing cross-domain connectivity.
Specifically, assuming partially overlapped items (users), superhighway
bypasses multi-hop inter-domain paths between cross-domain users (items,
respectively) with direct paths to enrich the cross-domain connectivity. The
experiments conducted on a real-world cross-region music dataset and a
cross-platform movie dataset show that the proposed superhighway construction
significantly improves recommendation performance in both target and source
domains.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Authors: Shi Yin, Yi Zhou, Chenguang Li, Shangfei Wang, Jianmin Ji, Xiaoping Chen, Ruili Wang
Date: 2018-08-28 00:00:00
Tasks: Word Sense Disambiguation

We propose KDSL, a new word sense disambiguation (WSD) framework that
utilizes knowledge to automatically generate sense-labeled data for supervised
learning. First, from WordNet, we automatically construct a semantic knowledge
base called DisDict, which provides refined feature words that highlight the
differences among word senses, i.e., synsets. Second, we automatically generate
new sense-labeled data by DisDict from unlabeled corpora. Third, these
generated data, together with manually labeled data and unlabeled data, are fed
to a neural framework conducting supervised and unsupervised learning jointly
to model the semantic relations among synsets, feature words and their
contexts. The experimental results show that KDSL outperforms several
representative state-of-the-art methods on various major benchmarks.
Interestingly, it performs relatively well even when manually labeled data is
unavailable, thus provides a potential solution for similar tasks in a lack of
manual annotations.
A Particle Filter based Multi-Objective Optimization Algorithm: PFOPS
Authors: Bin Liu, Yaochu Jin
Date: 2018-08-28 00:00:00

This paper is concerned with a recently developed paradigm for
population-based optimization, termed particle filter optimization (PFO). This
paradigm is attractive in terms of coherence in theory and easiness in
mathematical analysis and interpretation. Current PFO algorithms only work for
single-objective optimization cases, while many real-life problems involve
multiple objectives to be optimized simultaneously. To this end, we make an
effort to extend the scope of application of the PFO paradigm to
multi-objective optimization (MOO) cases. An idea called path sampling is
adopted within the PFO scheme to balance the different objectives to be
optimized. The resulting algorithm is thus termed PFO with Path Sampling
(PFOPS). The validity of the presented algorithm is assessed based on three
benchmark MOO experiments, in which the shapes of the Pareto fronts are convex,
concave and discontinuous, respectively.
An Adaptive Conversational Bot Framework
Authors: Isak Czeresnia Etinger
Date: 2018-08-27 00:00:00

How can we enable users to heavily specify criteria for database queries in a
user-friendly way? This paper describes a general framework of a conversational
bot that extracts meaningful information from user's sentences, that asks
subsequent questions to complete missing information, and that adjusts its
questions and information-extraction parameters for later conversations
depending on users' behavior. Additionally, we provide a comparison of existing
tools and give novel techniques to implement such framework. Finally, we
exemplify the framework with a bot to query movies in a database, whose code is
available for Microsoft employees.
Zero-shot Transfer Learning for Semantic Parsing
Authors: Javid Dadashkarimi, Alexander Fabbri, Sekhar Tatikonda, Dragomir R. Radev
Date: 2018-08-27 00:00:00
Tasks: Semantic Parsing, Transfer Learning

While neural networks have shown impressive performance on large datasets,
applying these models to tasks where little data is available remains a
challenging problem.
  In this paper we propose to use feature transfer in a zero-shot experimental
setting on the task of semantic parsing.
  We first introduce a new method for learning the shared space between
multiple domains based on the prediction of the domain label for each example.
  Our experiments support the superiority of this method in a zero-shot
experimental setting in terms of accuracy metrics compared to state-of-the-art
techniques.
  In the second part of this paper we study the impact of individual domains
and examples on semantic parsing performance.
  We use influence functions to this aim and investigate the sensitivity of
domain-label classification loss on each example.
  Our findings reveal that cross-domain adversarial attacks identify useful
examples for training even from the domains the least similar to the target
domain. Augmenting our training data with these influential examples further
boosts our accuracy at both the token and the sequence level.
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures
Authors: Ahmed Fares, Walid Gomaa, Mohamed A. Khamis
Date: 2018-08-27 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, Reinforcement Learning

The objective of this article is to optimize the overall traffic flow on
freeways using multiple ramp metering controls plus its complementary Dynamic
Speed Limits (DSLs). An optimal freeway operation can be reached when
minimizing the difference between the freeway density and the critical ratio
for maximum traffic flow. In this article, a Multi-Agent Reinforcement Learning
for Freeways Control (MARL-FWC) system for ramps metering and DSLs is proposed.
MARL-FWC introduces a new microscopic framework at the network level based on
collaborative Markov Decision Process modeling (Markov game) and an associated
cooperative Q-learning algorithm. The technique incorporates payoff propagation
(Max-Plus algorithm) under the coordination graphs framework, particularly
suited for optimal control purposes. MARL-FWC provides three control designs:
fully independent, fully distributed, and centralized; suited for different
network architectures. MARL-FWC was extensively tested in order to assess the
proposed model of the joint payoff, as well as the global payoff. Experiments
are conducted with heavy traffic flow under the renowned VISSIM traffic
simulator to evaluate MARL-FWC. The experimental results show a significant
decrease in the total travel time and an increase in the average speed (when
compared with the base case) while maintaining an optimal traffic flow.
Supervised Topic Models
Authors: David M. Blei, Jon D. McAuliffe
Date: 2010-03-03 00:00:00
Proceeding: NeurIPS 2007 12
Tasks: parameter estimation, regression, Topic Models

We introduce supervised latent Dirichlet allocation (sLDA), a statistical
model of labelled documents. The model accommodates a variety of response
types. We derive an approximate maximum-likelihood procedure for parameter
estimation, which relies on variational methods to handle intractable posterior
expectations. Prediction problems motivate this research: we use the fitted
model to predict response values for new documents. We test sLDA on two
real-world problems: movie ratings predicted from reviews, and the political
tone of amendments in the U.S. Senate based on the amendment text. We
illustrate the benefits of sLDA versus modern regularized regression, as well
as versus an unsupervised LDA analysis followed by a separate regression.
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
Authors: Chen Gao, Yuliang Zou, Jia-Bin Huang
Date: 2018-08-30 00:00:00
Tasks: Human-Object Interaction Detection, Object

Recent years have witnessed rapid progress in detecting and recognizing
individual object instances. To understand the situation in a scene, however,
computers need to recognize how humans interact with surrounding objects. In
this paper, we tackle the challenging task of detecting human-object
interactions (HOI). Our core idea is that the appearance of a person or an
object instance contains informative cues on which relevant parts of an image
to attend to for facilitating interaction prediction. To exploit these cues, we
propose an instance-centric attention module that learns to dynamically
highlight regions in an image conditioned on the appearance of each instance.
Such an attention-based network allows us to selectively aggregate features
relevant for recognizing HOIs. We validate the efficacy of the proposed network
on the Verb in COCO and HICO-DET datasets and show that our approach compares
favorably with the state-of-the-arts.
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation
Authors: Antonio Toral, Sheila Castilho, Ke Hu, Andy Way
Date: 2018-08-30 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

We reassess a recent study (Hassan et al., 2018) that claimed that machine
translation (MT) has reached human parity for the translation of news from
Chinese into English, using pairwise ranking and considering three variables
that were not taken into account in that previous study: the language in which
the source side of the test set was originally written, the translation
proficiency of the evaluators, and the provision of inter-sentential context.
If we consider only original source text (i.e. not translated from another
language, or translationese), then we find evidence showing that human parity
has not been achieved. We compare the judgments of professional translators
against those of non-experts and discover that those of the experts result in
higher inter-annotator agreement and better discrimination between human and
machine translations. In addition, we analyse the human translations of the
test set and identify important translation issues. Finally, based on these
findings, we provide a set of recommendations for future human evaluations of
MT.
Nested multi-instance classification
Authors: Alexander Stec, Diego Klabjan, Jean Utke
Date: 2018-08-30 00:00:00
Tasks: Classification, General Classification

There are classification tasks that take as inputs groups of images rather
than single images. In order to address such situations, we introduce a nested
multi-instance deep network. The approach is generic in that it is applicable
to general data instances, not just images. The network has several
convolutional neural networks grouped together at different stages. This
primarily differs from other previous works in that we organize instances into
relevant groups that are treated differently. We also introduce a method to
replace instances that are missing which successfully creates neutral input
instances and consistently outperforms standard fill-in methods in real world
use cases. In addition, we propose a method for manual dropout when a whole
group of instances is missing that allows us to use richer training data and
obtain higher accuracy at the end of training. With specific pretraining, we
find that the model works to great effect on our real world and pub-lic
datasets in comparison to baseline methods, justifying the different treatment
among groups of instances.