pwc-archive/papers-with-abstracts huggingface dataset:
Attention Modeling for Targeted Sentiment
Authors: Jiangming Liu, Yue Zhang
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Sentence, Sentiment Analysis, Word Embeddings

Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data
Authors: Heike Adel, Francine Chen, Yan-Ying Chen
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define the task of classifying the purchase stage of each tweet in a user{'}s tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.
Context-Aware Graph Segmentation for Graph-Based Translation
Authors: Liangyou Li, Andy Way, Qun Liu
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Segmentation, Translation

In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese{--}English and German{--}English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.
Reranking Translation Candidates Produced by Several Bilingual Word Similarity Sources
Authors: Laurent Jakubina, Phillippe Langlais
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Bilingual Lexicon Induction, Reranking, Translation, Word Embeddings, Word Similarity

We investigate the reranking of the output of several distributional approaches on the Bilingual Lexicon Induction task. We show that reranking an n-best list produced by any of those approaches leads to very substantial improvements. We further demonstrate that combining several n-best lists by reranking is an effective way of further boosting performance.
Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation
Authors: Maryam Siahbani, Anoop Sarkar
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Phrase-based and hierarchical phrase-based (Hiero) translation models differ radically in the way reordering is modeled. Lexicalized reordering models play an important role in phrase-based MT and such models have been added to CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary order and produces the translation in left to right (LR) order which leads to far fewer language model calls and leads to a considerable speedup in decoding. We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our lexicalized reordering model (LRM) and show that it improves translation quality for Czech-English, Chinese-English and German-English.
Bootstrapping Unsupervised Bilingual Lexicon Induction
Authors: Bradley Hauer, Garrett Nicolai, Grzegorz Kondrak
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Bilingual Lexicon Induction, Semantic Textual Similarity, Translation

The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on the basis of their frequency and lexical similarity. We apply bidirectional bootstrapping to a method which learns a linear mapping between context-based vector spaces. Experimental results on three language pairs show consistent improvement over prior work.
Addressing Problems across Linguistic Levels in SMT: Combining Approaches to Model Morphology, Syntax and Lexical Choice
Authors: Marion Weller-Di Marco, Alex Fraser, er, Sabine Schulte im Walde
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Alignment, Word Sense Disambiguation

Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice. We explore combinations of linguistically motivated approaches to address these problems in English-to-German SMT and show that they are complementary to one another, but also that the popular verbal pre-ordering can cause problems on the morphological and lexical level. A discriminative classifier can overcome these problems, in particular when enriching standard lexical features with features geared towards verbal inflection.
Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities
Authors: Ngoc Quang Luong, Andrei Popescu-Belis, Annette Rios Gonzales, Don Tuggener
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Coreference Resolution, Machine Translation, Translation

We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as {`}ella{'} vs. {`}she{'}/{`}it{'} or {`}su{'} vs. {`}his{'}/{`}her{'}/{`}its{'}/{`}their{'}. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83{\%}.
Using Images to Improve Machine-Translating E-Commerce Product Listings.
Authors: Iacer Calixto, Daniel Stein, Evgeny Matusov, Pintu Lohar, Sheila Castilho, Andy Way
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, NMT, Re-Ranking, Translation

In this paper we study the impact of using images to machine-translate user-generated e-commerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multi-modal NMT models for re-ranking $n$-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.
Continuous multilinguality with language vectors
Authors: Robert {\"O}stling, J{\"o}rg Tiedemann
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Image Captioning, Language Modeling, Language Modelling, Machine Translation, Speech Recognition

Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.
Co-reference Resolution of Elided Subjects and Possessive Pronouns in Spanish-English Statistical Machine Translation
Authors: Annette Rios Gonzales, Don Tuggener
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Coreference Resolution, Machine Translation, Translation

This paper presents a straightforward method to integrate co-reference information into phrase-based machine translation to address the problems of i) elided subjects and ii) morphological underspecification of pronouns when translating from pro-drop languages. We evaluate the method for the language pair Spanish-English and find that translation quality improves with the addition of co-reference information.
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models
Authors: Y Xia, i, Aaron Levine, Pradipto Das, Giuseppe Di Fabbrizio, Keiji Shinzato, Ankur Datta
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Feature Engineering, Product Categorization, Text Categorization

We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories. Compared to a state-of-the-art Gradient Boosted Tree (GBT) classifier, the proposed model reduces training time from three weeks to three days while maintaining more than 96{\%} accuracy. Additionally, our proposed model characterizes products by imputing attentive focus on word tokens in a language agnostic way. The attention words have been observed to be semantically highly correlated with the predicted categories and give us a choice of automatic feature extraction for downstream processing.
Convolutional Neural Networks for Authorship Attribution of Short Texts
Authors: Prasha Shrestha, Sebastian Sierra, Fabio Gonz{\'a}lez, Manuel Montes, Paolo Rosso, Thamar Solorio
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Authorship Attribution, General Classification, Language Modeling, Language Modelling

We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.
Aspect Extraction from Product Reviews Using Category Hierarchy Information
Authors: Yinfei Yang, Cen Chen, Minghui Qiu, Forrest Bao
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Aspect Extraction, Common Sense Reasoning, Opinion Mining

Aspect extraction abstracts the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of sub-categories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10{\%}.
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification
Authors: Juan Soler-Company, Leo Wanner
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Author Profiling, Feature Engineering

The majority of approaches to author profiling and author identification focus mainly on lexical features, i.e., on the content of a text. We argue that syntactic and discourse features play a significantly more prominent role than they were given in the past. We show that they achieve state-of-the-art performance in author and gender identification on a literary corpus while keeping the feature set small: the used feature set is composed of only 188 features and still outperforms the winner of the PAN 2014 shared task on author verification in the literary genre.
Unsupervised Cross-Lingual Scaling of Political Texts
Authors: Goran Glava{\v{s}}, Federico Nanni, Simone Paolo Ponzetto
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.
Multimodal Topic Labelling
Authors: Ionut Sorodoc, Jey Han Lau, Nikolaos Aletras, Timothy Baldwin
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Topic Models

Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose a multimodal approach to topic labelling using a simple feedforward neural network. Given a topic and a candidate image or textual label, our method automatically generates a rating for the label, relative to the topic. Experiments show that this multimodal approach outperforms single-modality topic labelling systems.
Discriminative Information Retrieval for Question Answering Sentence Selection
Authors: Tongfei Chen, Benjamin Van Durme
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Information Retrieval, Passage Retrieval, Question Answering, Retrieval, Sentence

We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34{\%}-43{\%} improvement in recall for candidate triage for QA.
Effective shared representations with Multitask Learning for Community Question Answering
Authors: Daniele Bonadiman, Antonio Uva, Aless Moschitti, ro
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Community Question Answering, Document Ranking, Named Entity Recognition (NER), Question Answering

An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features. Unfortunately, DNNs usually require a lot of training data, especially for highly semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20{\%} of relative improvement over standard DNNs.
Learning User Embeddings from Emails
Authors: Yan Song, Chia-Jung Lee
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Recommendation Systems, Word Embeddings

Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was overlooked. In this paper, we propose to represent users using embeddings that are trained to reflect the email communication network. Our experiments on Enron dataset suggest that the resulting embeddings capture the semantic distance between users. To assess the quality of embeddings in a real-world application, we carry out auto-foldering task where the lexical representation of an email is enriched with user embedding features. Our results show that folder prediction accuracy is improved when embedding features are present across multiple settings.
Temporal information extraction from clinical text
Authors: Julien Tourille, Olivier Ferret, Xavier Tannier, Aur{\'e}lie N{\'e}v{\'e}ol
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Relation, Relation Extraction, Temporal Information Extraction, Temporal Relation Extraction

In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages.
Neural Temporal Relation Extraction
Authors: Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, Guergana Savova
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Position, Relation, Relation Classification, Relation Extraction, Temporal Information Extraction, Temporal Relation Extraction

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.
End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Authors: Sanjeev Karn, Ulli Waltinger, Hinrich Sch{\"u}tze
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, Decoder, General Classification, Morphological Tagging

We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.
Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion
Authors: Ryan Cotterell, John Sylak-Glassman, Christo Kirov
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Morphological Analysis

Many of the world{'}s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages.
COVER: Covering the Semantically Tractable Questions
Authors: Michael Minock
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automated Theorem Proving, Semantic Parsing

In semantic parsing, natural language questions map to expressions in a meaning representation language (MRL) over some fixed vocabulary of predicates. To do this reliably, one must guarantee that for a wide class of natural language questions (the so called semantically tractable questions), correct interpretations are always in the mapped set of possibilities. In this demonstration, we introduce the system COVER which significantly clarifies, revises and extends the basic notion of semantic tractability. COVER achieves coverage of 89{\%} while the earlier PRECISE system achieved coverage of 77{\%} on the well known GeoQuery corpus. Like PRECISE, COVER requires only a simple domain lexicon and integrates off-the-shelf syntactic parsers. Beyond PRECISE, COVER also integrates off-the-shelf theorem provers to provide more accurate results. COVER is written in Python and uses the NLTK.
Common Round: Application of Language Technologies to Large-Scale Web Debates
Authors: Hans Uszkoreit, Aleks Gabryszak, ra, Leonhard Hennig, J{\"o}rg Steffen, Renlong Ai, Stephan Busemann, Jon Dehdari, Josef van Genabith, Georg Heigold, Nils Rethmeier, Raphael Rubino, Sven Schmeier, Philippe Thomas, He Wang, Feiyu Xu
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Decision Making, Machine Translation, Navigate, Translation

Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking. However, it is challenging to organize, structure, and navigate a vast number of diverse argumentations and comments collected from many participants over a long time period. In this paper we demonstrate Common Round, a next generation platform for large-scale web debates, which provides functions for eliciting the semantic content and structures from the contributions of participants. In particular, Common Round applies language technologies for the extraction of semantic essence from textual input, aggregation of the formulated opinions and arguments. The platform also provides a cross-lingual access to debates using machine translation.
A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets
Authors: Johann-Mattis List
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Alignment

The paper presents the Etymological DICtionary ediTOR (EDICTOR), a free, interactive, web-based tool designed to aid historical linguists in creating, editing, analysing, and publishing etymological datasets. The EDICTOR offers interactive solutions for important tasks in historical linguistics, including facilitated input and segmentation of phonetic transcriptions, quantitative and qualitative analyses of phonetic and morphological data, enhanced interfaces for cognate class assignment and multiple word alignment, and automated evaluation of regular sound correspondences. As a web-based tool written in JavaScript, the EDICTOR can be used in standard web browsers across all major platforms.