pwc-archive/papers-with-abstracts huggingface dataset:
Identifying the Provision of Choices in Privacy Policy Text
Authors: Kanthashree Mysore Sathyendra, Shomir Wilson, Florian Schaub, Sebastian Zimmeck, Norman Sadeh
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering

Websites{'} and mobile apps{'} privacy policies, written in natural language, tend to be long and difficult to understand. Information privacy revolves around the fundamental principle of Notice and choice, namely the idea that users should be able to make informed decisions about what information about them can be collected and how it can be used. Internet users want control over their privacy, but their choices are often hidden in long and convoluted privacy policy texts. Moreover, little (if any) prior work has been done to detect the provision of choices in text. We address this challenge of enabling user choice by automatically identifying and extracting pertinent choice language in privacy policies. In particular, we present a two-stage architecture of classification models to identify opt-out choices in privacy policy text, labelling common varieties of choices with a mean F1 score of 0.735. Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.
An Empirical Analysis of Edit Importance between Document Versions
Authors: Tanya Goyal, Sachin Kelkar, Manas Agarwal, Jeenu Grover
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Summarization, regression

In this paper, we present a novel approach to infer significance of various textual edits to documents. An author may make several edits to a document; each edit varies in its impact to the content of the document. While some edits are surface changes and introduce negligible change, other edits may change the content/tone of the document significantly. In this paper, we perform an analysis on the human perceptions of edit importance while reviewing documents from one version to the next. We identify linguistic features that influence edit importance and model it in a regression based setting. We show that the predicted importance by our approach is highly correlated with the human perceived importance, established by a Mechanical Turk study.
Transition-Based Disfluency Detection using LSTMs
Authors: Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, Ting Liu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Information Retrieval

In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-the-art f-score of 87.5{\%} on the commonly used English Switchboard test set, and a set of in-house annotated Chinese data.
Neural Sequence-Labelling Models for Grammatical Error Correction
Authors: Helen Yannakoudakis, Marek Rei, {\O}istein E. Andersen, Zheng Yuan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Grammatical Error Correction, Machine Translation, Reranking, Sentence, Translation

We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.
A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Authors: Xing Niu, Marianna Martindale, Marine Carpuat
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Domain Adaptation, Machine Translation, Translation

Stylistic variations of language, such as formality, carry speakers{'} intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
Authors: Joo-Kyung Kim, Young-Bum Kim, Ruhi Sarikaya, Eric Fosler-Lussier
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Cross-Lingual Transfer, Language Modeling, Language Modelling, Named Entity Recognition (NER), Part-Of-Speech Tagging, POS, POS Tagging, Slot Filling, Transfer Learning, Word Embeddings

Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.
Neural Machine Translation with Source Dependency Representation
Authors: Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, NMT, Translation

Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.
Visual Denotations for Recognizing Textual Entailment
Authors: Dan Han, Pascual Mart{\'\i}nez-G{\'o}mez, Koji Mineshima
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Natural Language Inference, Semantic Composition

In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.
Sequence Effects in Crowdsourced Annotations
Authors: Nitika Mathur, Timothy Baldwin, Trevor Cohn
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Manual data annotation is a vital component of NLP research. When designing annotation tasks, properties of the annotation interface can unintentionally lead to artefacts in the resulting dataset, biasing the evaluation. In this paper, we explore sequence effects where annotations of an item are affected by the preceding items. Having assigned one label to an instance, the annotator may be less (or more) likely to assign the same label to the next. During rating tasks, seeing a low quality item may affect the score given to the next item either positively or negatively. We see clear evidence of both types of effects using auto-correlation studies over three different crowdsourced datasets. We then recommend a simple way to minimise sequence effects.
No Need to Pay Attention: Simple Recurrent Neural Networks Work!
Authors: Ferhan Ture, Oliver Jojic
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering

First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65{\%}{--}76{\%} accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results {---} even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast{'}s X1 entertainment platform with millions of users every day.
The strange geometry of skip-gram with negative sampling
Authors: David Mimno, Laure Thompson
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
Authors: Xin Li, Wai Lam
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Aspect-Based Sentiment Analysis (ABSA), Multi-Task Learning, Sentence, Sentiment Analysis, Term Extraction

We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia
Authors: E. Dar{\'\i}o Guti{\'e}rrez, Guillermo Cecchi, Cheryl Corcoran, Philip Corlett
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Sentiment Analysis

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
Authors: Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, Yejin Choi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Fact Checking, Fake News Detection

We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
Topic-Based Agreement and Disagreement in US Electoral Manifestos
Authors: Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, Sara Tonelli
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Clustering

We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora
Authors: Hainan Xu, Philipp Koehn
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Outlier Detection, regression, Translation

We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.
Natural Language Does Not Emerge `Naturally' in Multi-Agent Dialog
Authors: Satwik Kottur, Jos{\'e} Moura, Stefan Lee, Dhruv Batra
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Slot Filling

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task {\&} Talk reference game between two agents as a testbed, we present a sequence of {`}negative{'} results culminating in a {`}positive{'} one {--} showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge {`}naturally{'},despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts
Authors: Cecilia Ovesdotter Alm, Benjamin Meyers, Emily Prud{'}hommeaux
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Active Learning

We present an educational tool that integrates computational linguistics resources for use in non-technical undergraduate language science courses. By using the tool in conjunction with evidence-driven pedagogical case studies, we strive to provide opportunities for students to gain an understanding of linguistic concepts and analysis through the lens of realistic problems in feasible ways. Case studies tend to be used in legal, business, and health education contexts, but less in the teaching and learning of linguistics. The approach introduced also has potential to encourage students across training backgrounds to continue on to computational language analysis coursework.
GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques
Authors: Tobias Falke, Iryna Gurevych
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Navigate

Graphs have long been proposed as a tool to browse and navigate in a collection of documents in order to support exploratory search. Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested. While experimental evidence that they are indeed helpful exists for some of them, it is largely unknown which type of graph is most helpful for a specific exploratory task. However, carrying out experimental comparisons with human subjects is challenging and time-consuming. Towards this end, we present the \textit{GraphDocExplore} framework. It provides an intuitive web interface for graph-based document exploration that is optimized for experimental user studies. Through a generic graph interface, different methods to extract graphs from text can be plugged into the system. Hence, they can be compared at minimal implementation effort in an environment that ensures controlled comparisons. The system is publicly available under an open-source license.
StruAP: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern
Authors: Kohsuke Yanai, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Yuta Koreeda, Yoshiki Niwa
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decision Making, Information Retrieval, Relation Extraction

We present a tool for developing tree structure patterns that makes it easy to define the relations among textual phrases and create a search index for these newly defined relations. By using the proposed tool, users develop tree structure patterns through abstracting syntax trees. The tool features (1) intuitive pattern syntax, (2) unique functions such as recursive call of patterns and use of lexicon dictionaries, and (3) whole workflow support for relation development and validation. We report the current implementation of the tool and its effectiveness.
KnowYourNyms? A Game of Semantic Relationships
Authors: Ross Mechanic, Dean Fulgoni, Hannah Cutler, Sneha Rajana, Zheyuan Liu, Bradley Jackson, Anne Cocos, Chris Callison-Burch, Marianna Apidianaki
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Natural Language Understanding, Relation, Text Classification

Semantic relation knowledge is crucial for natural language understanding. We introduce {``}KnowYourNyms?{''}, a web-based game for learning semantic relations. While providing users with an engaging experience, the application collects large amounts of data that can be used to improve semantic relation classifiers. The data also broadly informs us of how people perceive the relationships between words, providing useful insights for research in psychology and linguistics.
The Projector: An Interactive Annotation Projection Visualization Tool
Authors: Alan Akbik, Rol Vollgraf
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Previous works proposed annotation projection in parallel corpora to inexpensively generate treebanks or propbanks for new languages. In this approach, linguistic annotation is automatically transferred from a resource-rich source language (SL) to translations in a target language (TL). However, annotation projection may be adversely affected by translational divergences between specific language pairs. For this reason, previous work often required careful qualitative analysis of projectability of specific annotation in order to define strategies to address quality and coverage issues. In this demonstration, we present THE PROJECTOR, an interactive GUI designed to assist researchers in such analysis: it allows users to execute and visually inspect annotation projection in a range of different settings. We give an overview of the GUI, discuss use cases and illustrate how the tool can facilitate discussions with the research community.
Interactive Visualization for Linguistic Structure
Authors: Aaron Sarnat, Vidur Joshi, Cristian Petrescu-Prahova, Alvaro Herrasti, Br Stilson, on, Mark Hopkins
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a general-purpose API for the interactive exploration of hierarchical linguistic structure. To facilitate rapid understanding of a complex structure, the API offers several important features, including expand/collapse functionality, positional and color cues, explicit visual support for sequential structure, and dynamic highlighting to convey node-to-text correspondence.
DLATK: Differential Language Analysis ToolKit
Authors: H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, Johannes Eichstaedt
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification

We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.
QUINT: Interpretable Question Answering over Knowledge Bases
Authors: Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Named Entity Recognition (NER), Question Answering, Semantic Parsing

We present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.