Articles

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact on event coreference reso- lution and extraction, there has been relatively little work on computational methods for iden- tifying and …...

We present a probabilistic model of the in- fluence of language on the behavior of the U.S. Supreme Court, specifically influence of amicus briefs on Court decisions and opin- ions. The approach assumes that amici are rational, utility-maximizing agents who try to win votes or affect the language of court …...

We present a framework for augmenting data sets from the Universal Dependencies project with Universal Decompositional Semantics. Where the Universal Dependencies project aims to provide a syntactic annotation stan- dard that can be used consistently across many languages as well as a collection of corpora that use that standard, our …...

Syntactic parsing of web queries is impor- tant for query understanding. However, web queries usually do not observe the grammar of a written language, and no labeled syntactic trees for web queries are available. In this pa- per, we focus on a query’s clicked sentence, i.e., a well-formed sentence that …...

Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the exist- ing methods encode two sequences with sepa- rate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose …...

One major deficiency of most semantic repre- sentation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individ- ual meanings of words has been …...

Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and in- terpretability. We develop a general frame- work that enables learning knowledge and its confidence jointly with the DNNs, so that the vast amount of fuzzy knowledge can be incor- …...

Distributional models are derived from co- occurrences in a corpus, where only a small proportion of all possible plausible co- occurrences will be observed. This results in a very sparse vector space, requiring a mechanism for inferring missing knowledge. Most methods face this challenge in ways that render the resulting …...

Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. How- ever, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit. To address this problem, …...

Document-level sentiment classification aims to predict user’s overall sentiment in a doc- ument about a product. However, most of existing methods only focus on local text in- formation and ignore the global user pref- erence and product characteristics. Even though some works take such information into account, they usually suffer …...

In this paper, we present our work in emo- tion cause extraction. Since there is no open dataset available, the lack of annotated re- sources has limited the research in this area. Thus, we first present a dataset we built using SINA city news. The annotation is based on the …...

Texts present coherent stories that have a par- ticular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called rewrit- ing that edits existing human-authored narra- tives to change their theme without changing the underlying story. We apply the approach …...

To learn text understanding models with millions of parameters one needs massive amounts of data. In this work, we argue that generating data can compensate for this need. While defining generic data generators is dif- ficult, we propose to allow generators to be “weakly” specified in the sense that a …...

We address the problem of answering new questions in community forums, by selecting suitable answers to already asked questions. We approach the task as an answer ranking problem, adopting a pairwise neural network architecture that selects which of two compet- ing answers is better. We focus on the util- ity …...

We show that a character-level encoder- decoder framework can be successfully ap- plied to question answering with a structured knowledge base. We use our model for single- relation question answering and demonstrate the effectiveness of our approach on the Sim- pleQuestions dataset (Bordes et al., 2015), where we improve state-of-the-art …...

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present …...

The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that signifi- cantly improves BLEU scores across a range of low-resource languages. Our key idea is to first train a …...

Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understand- ing the meaning of the sentence. We propose a method to alleviate this problem by aug- menting NMT systems with discrete transla- tion lexicons that efficiently encode transla- tions of these low-frequency words. …...

Sentiment lexicons have been leveraged as a useful source of features for sentiment anal- ysis models, leading to the state-of-the-art accuracies. On the other hand, most ex- isting methods use sentiment lexicons with- out considering context, typically taking the count, sum of strength, or maximum senti- ment scores over the …...

In this work, we study parameter tuning to- wards the M2 metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M2 as a scorer in the Moses tuning framework, we investi- gate interactions of dense and sparse features, different optimizers, and tuning strategies for the CoNLL-2014 …...