Articles

This paper presents METEOR-NEXT, an ex- tended version of the METEOR metric de- signed to have high correlation with post- editing measures of machine translation qual- ity. We describe changes made to the met- ric’s sentence aligner and scoring scheme as well as a method for tuning the metric’s pa- …...

Semantic role labeling (SRL) not only needs lexical and syntactic information, but also needs word sense information. However, be- cause of the lack of corpus annotated with both word senses and semantic roles, there is few research on using word sense for SRL. The release of OntoNotes provides an oppor- …...

This paper describes an efficient sampler for synchronous grammar induction under a non- parametric Bayesian prior. Inspired by ideas from slice sampling, our sampler is able to draw samples from the posterior distributions of models for which the standard dynamic pro- graming based sampler proves intractable on non-trivial corpora. We …...

We present a Gaussian Mixture model for de- tecting different types of figurative language in context. We show that this model performs well when the parameters are estimated in an unsupervised fashion using EM. Performance can be improved further by estimating the pa- rameters from a small annotated data set. …...

Language identification is the task of identify- ing the language a given document is written in. This paper describes a detailed examina- tion of what models perform best under dif- ferent conditions, based on experiments across three separate datasets and a range of tokeni- sation strategies. We demonstrate that the …...

A variety of information extraction techniques rely on the fact that instances of the same relation are “distributionally similar,” in that they tend to appear in similar textual con- texts. We demonstrate that extraction accu- racy depends heavily on the accuracy of the language model utilized to estimate distribu- tional …...

Out-of-vocabulary (OOV) words represent an important source of error in large vocabulary continuous speech recognition (LVCSR) sys- tems. These words cause recognition failures, which propagate through pipeline systems im- pacting the performance of downstream ap- plications. The detection of OOV regions in the output of a LVCSR system is typically …...

Deploying an automatic speech recogni- tion system with reasonable performance requires expensive and time-consuming in-domain transcription. Previous work demonstrated that non-professional anno- tation through Amazon’s Mechanical Turk can match professional quality. We use Mechanical Turk to transcribe conversa- tional speech for as little as one thir- tieth the cost of …...

We conduct a pilot study for task-oriented evaluation of Multiword Expression (MWE) in Statistical Machine Translation (SMT). We propose two different integration strategies for MWE in SMT, which take advantage of differ- ent degrees of MWE semantic compositional- ity and yield complementary improvements in SMT quality on a large-scale translation …...

We address the problem of formatting the out- put of an automatic speech recognition (ASR) system for readability, while preserving word- level timing information of the transcript. Our system enriches the ASR transcript with punc- tuation, capitalization and properly written dates, times and other numeric entities, and our approach can …...

With the recent rise in popularity and size of social media, there is a growing need for sys- tems that can extract useful information from this amount of data. We address the prob- lem of detecting new events from a stream of Twitter posts. To make event detection feasi- ble …...

We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method dis- covers dialogue acts by clustering raw utter- ances. Because it accounts for the sequential behaviour of these acts, the learned model can …...

We present results from a range of experi- ments on article and preposition error correc- tion for non-native speakers of English. We first compare a language model and error- specific classifiers (all trained on large Eng- lish corpora) with respect to their performance in error detection and correction. We then …...

This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the train- ing data. When training a classifier, we would like the distribution of examples seen in train- ing to be as similar …...

We present a random-walk-based approach to learning paraphrases from bilingual parallel corpora. The corpora are represented as a graph in which a node corresponds to a phrase, and an edge exists between two nodes if their corresponding phrases are aligned in a phrase table. We sample random walks to compute …...

Constrained decoding is of great importance not only for speed but also for translation qual- ity. Previous efforts explore soft syntactic con- straints which are based on constituent bound- aries deduced from parse trees of the source language. We present a new framework to es- tablish soft constraints based on …...

In this paper we propose a novel general framework for unsupervised model adapta- tion. Our method is based on entropy which has been used previously as a regularizer in semi-supervised learning. This technique in- cludes another term which measures the sta- bility of posteriors w.r.t model parameters, in addition to …...

Syntactic machine translation systems cur- rently use word alignments to infer syntactic correspondences between the source and tar- get languages. Instead, we propose an un- supervised ITG alignment model that directly aligns syntactic structures. Our model aligns spans in a source sentence to nodes in a target parse tree. We …...

Syntactic machine translation systems extract rules from bilingual, word-aligned, syntacti- cally parsed text, but current systems for pars- ing and word alignment are at best cascaded and at worst totally independent of one an- other. This work presents a unified joint model for simultaneous parsing and word alignment. To flexibly …...

Current vector-space models of lexical seman- tics create a single “prototype” vector to rep- resent the meaning of a word. However, due to lexical ambiguity, encoding word mean- ing with a single vector is problematic. This paper presents a method that uses cluster- ing to produce multiple “sense-specific” vec- tors …...