pwc-archive/papers-with-abstracts huggingface dataset:
Identifying Well-formed Natural Language Questions
Authors: Manaal Faruqui, Dipanjan Das
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Query Wellformedness

Understanding search queries is a hard problem as it involves dealing with
"word salad" text ubiquitously issued by users. However, if a query resembles a
well-formed question, a natural language processing pipeline is able to perform
more accurate interpretation, thus reducing downstream compounding errors.
Hence, identifying whether or not a query is well formed can enhance query
understanding. Here, we introduce a new task of identifying a well-formed
natural language question. We construct and release a dataset of 25,100
publicly available questions classified into well-formed and non-wellformed
categories and report an accuracy of 70.7% on the test set. We also show that
our classifier can be used to improve the performance of neural
sequence-to-sequence models for generating questions for reading comprehension.
Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts
Authors: Charles Hessel, Jean-Michel Morel
Date: 2018-08-28 00:00:00
Tasks: Tone Mapping, valid

This paper introduces a quantitative evaluation of filters that seek to
separate an image into its large-scale variations, the base layer, and its
fine-scale variations, the detail layer. Such methods have proliferated with
the development of HDR imaging and the proposition of many new tone-mapping
operators. We argue that an objective quality measurement for all methods can
be based on their artifacts. To this aim, the four main recurrent artifacts are
described and mathematically characterized. Among them two are classic, the
luminance halo and the staircase effect, but we show the relevance of two more,
the contrast halo and the compartmentalization effect. For each of these
artifacts we design a test-pattern and its attached measurement formula. Then
we fuse these measurements into a single quality mark, and obtain in that way a
ranking method valid for all filters performing a base+detail decomposition.
This synthetic ranking is applied to seven filters representative of the
literature and shown to agree with expert artifact rejection criteria.
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data
Authors: Zi Lin, Yuguang Duan, Yuan-Yuan Zhao, Weiwei Sun, Xiaojun Wan
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Parsing, Semantic Role Labeling, Sentence

This paper studies semantic parsing for interlanguage (L2), taking semantic
role labeling (SRL) as a case task and learner Chinese as a case language. We
first manually annotate the semantic roles for a set of learner texts to derive
a gold standard for automatic SRL. Based on the new data, we then evaluate
three off-the-shelf SRL systems, i.e., the PCFGLA-parser-based,
neural-parser-based and neural-syntax-agnostic systems, to gauge how successful
SRL for learner Chinese can be. We find two non-obvious facts: 1) the
L1-sentence-trained systems performs rather badly on the L2 data; 2) the
performance drop from the L1 data to the L2 data of the two parser-based
systems is much smaller, indicating the importance of syntactic parsing in SRL
for interlanguages. Finally, the paper introduces a new agreement-based model
to explore the semantic coherency information in the large-scale L2-L1 parallel
data. We then show such information is very effective to enhance SRL for
learner texts. Our model achieves an F-score of 72.06, which is a 2.02 point
improvement over the best baseline.
Privacy-preserving Neural Representations of Text
Authors: Maximin Coavoux, Shashi Narayan, Shay B. Cohen
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Privacy Preserving

This article deals with adversarial attacks towards deep learning systems for
Natural Language Processing (NLP), in the context of privacy protection. We
study a specific type of attack: an attacker eavesdrops on the hidden
representations of a neural text classifier and tries to recover information
about the input text. Such scenario may arise in situations when the
computation of a neural network is shared across multiple devices, e.g. some
hidden representation is computed by a user's device and sent to a cloud-based
model. We measure the privacy of a hidden representation by the ability of an
attacker to predict accurately specific private information from it and
characterize the tradeoff between the privacy and the utility of neural
representations. Finally, we propose several defense methods based on modified
training objectives and show that they improve the privacy of neural
representations.
Temporal Information Extraction by Predicting Relative Time-lines
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Temporal Information Extraction

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine
Authors: Dongdong Chen, JIancheng Lv, Mike E. Davies
Date: 2018-08-28 00:00:00
Tasks: Representation Learning

We investigate the potential of a restricted Boltzmann Machine (RBM) for
discriminative representation learning. By imposing the class information
preservation constraints on the hidden layer of the RBM, we propose a Signed
Laplacian Restricted Boltzmann Machine (SLRBM) for supervised discriminative
representation learning. The model utilizes the label information and preserves
the global data locality of data points simultaneously. Experimental results on
the benchmark data set show the effectiveness of our method.
Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies
Authors: Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, Yulia Tsvetkov
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles

Amidst growing concern over media manipulation, NLP attention has focused on
overt strategies like censorship and "fake news'". Here, we draw on two
concepts from the political science literature to explore subtler strategies
for government media manipulation: agenda-setting (selecting what topics to
cover) and framing (deciding how topics are covered). We analyze 13 years (100K
articles) of the Russian newspaper Izvestia and identify a strategy of
distraction: articles mention the U.S. more frequently in the month directly
following an economic downturn in Russia. We introduce embedding-based methods
for cross-lingually projecting English frames to Russian, and discover that
these articles emphasize U.S. moral failings and threats to the U.S. Our work
offers new ways to identify subtle media manipulation strategies at the
intersection of agenda-setting and framing.
What Makes Reading Comprehension Questions Easier?
Authors: Saku Sugawara, Kentaro Inui, Satoshi Sekine, Akiko Aizawa
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Reading Comprehension, Multiple-choice, Reading Comprehension, Sentence

A challenge in creating a dataset for machine reading comprehension (MRC) is
to collect questions that require a sophisticated understanding of language to
answer beyond using superficial cues. In this work, we investigate what makes
questions easier across recent 12 MRC datasets with three question styles
(answer extraction, description, and multiple choice). We propose to employ
simple heuristics to split each dataset into easy and hard subsets and examine
the performance of two baseline models for each of the subsets. We then
manually annotate questions sampled from each subset with both validity and
requisite reasoning skills to investigate which skills explain the difference
between easy and hard questions. From this study, we observed that (i) the
baseline performances for the hard subsets remarkably degrade compared to those
of entire datasets, (ii) hard questions require knowledge inference and
multiple-sentence reasoning in comparison with easy questions, and (iii)
multiple-choice questions tend to require a broader range of reasoning skills
than answer extraction and description questions. These results suggest that
one might overestimate recent advances in MRC.
Understanding Back-Translation at Scale
Authors: Sergey Edunov, Myle Ott, Michael Auli, David Grangier
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

An effective method to improve neural machine translation with monolingual
data is to augment the parallel training corpus with back-translations of
target language sentences. This work broadens the understanding of
back-translation and investigates a number of methods to generate synthetic
source sentences. We find that in all but resource poor settings
back-translations obtained via sampling or noised beam outputs are most
effective. Our analysis shows that sampling or noisy synthetic data gives a
much stronger training signal than data generated by beam or greedy search. We
also compare how synthetic data compares to genuine bitext and study various
domain effects. Finally, we scale to hundreds of millions of monolingual
sentences and achieve a new state of the art of 35 BLEU on the WMT'14
English-German test set.
A Tree-based Decoder for Neural Machine Translation
Authors: Xinyi Wang, Hieu Pham, Pengcheng Yin, Graham Neubig
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, NMT, Open-Ended Question Answering, Translation

Recent advances in Neural Machine Translation (NMT) show that adding
syntactic information to NMT systems can improve the quality of their
translations. Most existing work utilizes some specific types of
linguistically-inspired tree structures, like constituency and dependency parse
trees. This is often done via a standard RNN decoder that operates on a
linearized target tree structure. However, it is an open question of what
specific linguistic formalism, if any, is the best structural representation
for NMT. In this paper, we (1) propose an NMT model that can naturally generate
the topology of an arbitrary tree structure on the target side, and (2)
experiment with various target tree structures. Our experiments show the
surprising result that our model delivers the best improvements with balanced
binary trees constructed without any linguistic knowledge; this model
outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods
for incorporating target-side syntax by up to 0.7 BLEU.
Mean Field Analysis of Neural Networks: A Central Limit Theorem
Authors: Justin Sirignano, Konstantinos Spiliopoulos
Date: 2018-08-28 00:00:00
Tasks: Speech Recognition

We rigorously prove a central limit theorem for neural network models with a single hidden layer. The central limit theorem is proven in the asymptotic regime of simultaneously (A) large numbers of hidden units and (B) large numbers of stochastic gradient descent training iterations. Our result describes the neural network's fluctuations around its mean-field limit. The fluctuations have a Gaussian distribution and satisfy a stochastic partial differential equation. The proof relies upon weak convergence methods from stochastic analysis. In particular, we prove relative compactness for the sequence of processes and uniqueness of the limiting process in a suitable Sobolev space.
Matrix Factorization Equals Efficient Co-occurrence Representation
Authors: Farhan Khawar, Nevin L. Zhang
Date: 2018-08-28 00:00:00
Tasks: Diversity

Matrix factorization is a simple and effective solution to the recommendation
problem. It has been extensively employed in the industry and has attracted
much attention from the academia. However, it is unclear what the
low-dimensional matrices represent. We show that matrix factorization can
actually be seen as simultaneously calculating the eigenvectors of the
user-user and item-item sample co-occurrence matrices. We then use insights
from random matrix theory (RMT) to show that picking the top eigenvectors
corresponds to removing sampling noise from user/item co-occurrence matrices.
Therefore, the low-dimension matrices represent a reduced noise user and item
co-occurrence space. We also analyze the structure of the top eigenvector and
show that it corresponds to global effects and removing it results in less
popular items being recommended. This increases the diversity of the items
recommended without affecting the accuracy.
Deriving Machine Attention from Human Rationales
Authors: Yujia Bao, Shiyu Chang, Mo Yu, Regina Barzilay
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10

Attention-based models are successful when trained on large amounts of data.
In this paper, we demonstrate that even in the low-resource scenario, attention
can be learned effectively. To this end, we start with discrete human-annotated
rationales and map them into continuous attention. Our central hypothesis is
that this mapping is general across domains, and thus can be transferred from
resource-rich domains to low-resource ones. Our model jointly learns a
domain-invariant representation and induces the desired mapping between
rationales and attention. Our empirical results validate this hypothesis and
show that our approach delivers significant gains over state-of-the-art
baselines, yielding over 15% average error reduction on benchmark datasets.
Rational Recurrences
Authors: Hao Peng, Roy Schwartz, Sam Thomson, Noah A. Smith
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, text-classification, Text Classification

Despite the tremendous empirical success of neural models in natural language
processing, many of them lack the strong intuitions that accompany classical
machine learning approaches. Recently, connections have been shown between
convolutional neural networks (CNNs) and weighted finite state automata
(WFSAs), leading to new interpretations and insights. In this work, we show
that some recurrent neural networks also share this connection to WFSAs. We
characterize this connection formally, defining rational recurrences to be
recurrent hidden state update functions that can be written as the Forward
calculation of a finite set of WFSAs. We show that several recent neural models
use rational recurrences. Our analysis provides a fresh view of these models
and facilitates devising new neural architectures that draw inspiration from
WFSAs. We present one such model, which performs better than two recent
baselines on language modeling and text classification. Our results demonstrate
that transferring intuitions from classical models like WFSAs can be an
effective approach to designing and understanding neural models.
Universal Dependency Parsing with a General Transition-Based DAG Parser
Authors: Daniel Hershcovich, Omri Abend, Ari Rappoport
Date: 2018-08-28 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD
shared task. TUPA is a general neural transition-based DAG parser, which we use
to present the first experiments on recovering enhanced dependencies as part of
the general parsing task. TUPA was designed for parsing UCCA, a
cross-linguistic semantic annotation scheme, exhibiting reentrancy,
discontinuity and non-terminal nodes. By converting UD trees and graphs to a
UCCA-like DAG format, we train TUPA almost without modification on the UD
parsing task. The generic nature of our approach lends itself naturally to
multitask learning. Our code is available at
https://github.com/CoNLL-UD-2018/HUJI
Xu: An Automated Query Expansion and Optimization Tool
Authors: Morgan Gallant, Haruna Isah, Farhana Zulkernine, Shahzad Khan
Date: 2018-08-28 00:00:00
Tasks: Articles, Clustering, Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

The exponential growth of information on the Internet is a big challenge for information retrieval systems towards generating relevant results. Novel approaches are required to reformat or expand user queries to generate a satisfactory response and increase recall and precision. Query expansion (QE) is a technique to broaden users' queries by introducing additional tokens or phrases based on some semantic similarity metrics. The tradeoff is the added computational complexity to find semantically similar words and a possible increase in noise in information retrieval. Despite several research efforts on this topic, QE has not yet been explored enough and more work is needed on similarity matching and composition of query terms with an objective to retrieve a small set of most appropriate responses. QE should be scalable, fast, and robust in handling complex queries with a good response time and noise ceiling. In this paper, we propose Xu, an automated QE technique, using high dimensional clustering of word vectors and Datamuse API, an open source query engine to find semantically similar words. We implemented Xu as a command line tool and evaluated its performances using datasets containing news articles and human-generated QEs. The evaluation results show that Xu was better than Datamuse by achieving about 88% accuracy with reference to the human-generated QE.
Evaluating Theory of Mind in Question Answering
Authors: Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering

We propose a new dataset for evaluating question answering models with
respect to their capacity to reason about beliefs. Our tasks are inspired by
theory-of-mind experiments that examine whether children are able to reason
about the beliefs of others, in particular when those beliefs differ from
reality. We evaluate a number of recent neural models with memory augmentation.
We find that all fail on our tasks, which require keeping track of inconsistent
states of the world; moreover, the models' accuracy decreases notably when
random sentences are introduced to the tasks at test.
3D-Aware Scene Manipulation via Inverse Graphics
Authors: Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T. Freeman, Joshua B. Tenenbaum
Date: 2018-08-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Disentanglement, Object

We aim to obtain an interpretable, expressive, and disentangled scene
representation that contains comprehensive structural and textural information
for each object. Previous scene representations learned by neural networks are
often uninterpretable, limited to a single object, or lacking 3D knowledge. In
this work, we propose 3D scene de-rendering networks (3D-SDN) to address the
above issues by integrating disentangled representations for semantics,
geometry, and appearance into a deep generative model. Our scene encoder
performs inverse graphics, translating a scene into a structured object-wise
representation. Our decoder has two components: a differentiable shape renderer
and a neural texture generator. The disentanglement of semantics, geometry, and
appearance supports 3D-aware scene manipulation, e.g., rotating and moving
objects freely while keeping the consistent shape and texture, and changing the
object appearance without affecting its shape. Experiments demonstrate that our
editing scheme based on 3D-SDN is superior to its 2D counterpart.
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
Authors: Chao Chen, Zhihong Chen, Boyuan Jiang, Xinyu Jin
Date: 2018-08-28 00:00:00
Tasks: Domain Adaptation

Recently, considerable effort has been devoted to deep domain adaptation in
computer vision and machine learning communities. However, most of existing
work only concentrates on learning shared feature representation by minimizing
the distribution discrepancy across different domains. Due to the fact that all
the domain alignment approaches can only reduce, but not remove the domain
shift. Target domain samples distributed near the edge of the clusters, or far
from their corresponding class centers are easily to be misclassified by the
hyperplane learned from the source domain. To alleviate this issue, we propose
to joint domain alignment and discriminative feature learning, which could
benefit both domain alignment and final classification. Specifically, an
instance-based discriminative feature learning method and a center-based
discriminative feature learning method are proposed, both of which guarantee
the domain invariant features with better intra-class compactness and
inter-class separability. Extensive experiments show that learning the
discriminative features in the shared feature space can significantly boost the
performance of deep domain adaptation methods.
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction
Authors: Sebastian Ruder, Ryan Cotterell, Yova Kementchedjhieva, Anders Søgaard
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Bilingual Lexicon Induction

We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a representation-based approach (Artetxe et al., 2017). To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical results on six language pairs under two metrics and show that the prior improves the induced bilingual lexicons. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models
Authors: Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Peter Clark
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Inference

Most textual entailment models focus on lexical gaps between the premise text
and the hypothesis, but rarely on knowledge gaps. We focus on filling these
knowledge gaps in the Science Entailment task, by leveraging an external
structured knowledge base (KB) of science facts. Our new architecture combines
standard neural entailment models with a knowledge lookup module. To facilitate
this lookup, we propose a fact-level decomposition of the hypothesis, and
verifying the resulting sub-facts against both the textual premise and the
structured KB. Our model, NSnet, learns to aggregate predictions from these
heterogeneous data formats. On the SciTail dataset, NSnet outperforms a simpler
combination of the two predictions by 3% and the base entailment model by 5%.