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Improving Statistical Machine Translation Using Word Sense Disambiguation
We show for the first time that incorporating the predictions of a word sense disambiguation system within a typical phrase-based statistical machine translation (SMT) model consistently improvesExpand
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Word Sense Disambiguation vs. Statistical Machine Translation
We directly investigate a subject of much recent debate: do word sense disambiguation models help statistical machine translation quality? We present empirical results casting doubt on this common,Expand
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The NRC System for Discriminating Similar Languages
We describe the system built by the National Research Council Canada for the ”Discriminating between similar languages” (DSL) shared task. Our system uses various statistical classifiers and makesExpand
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Task-based Evaluation of Multiword Expressions: a Pilot Study in Statistical Machine Translation
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, whichExpand
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Measuring Machine Translation Errors in New Domains
We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-levelExpand
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A Stacked, Voted, Stacked Model for Named Entity Recognition
This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several effective approaches,Expand
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SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
This task combines the labeling of multiword expressions and supersenses (coarse-grained classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine systems participated; theExpand
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How Phrase Sense Disambiguation outperforms Word Sense Disambiguation for Statistical Machine Translation
We present comparative empirical evidence arguing that a generalized phrase sense disambiguation approach better improves statistical machine translation than ordinary word sense disambiguation,Expand
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One Translation Per Discourse
We revisit the one sense per discourse hypothesis of Gale et al. in the context of machine translation. Since a given sense can be lexicalized differently in translation, do we observe oneExpand
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An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during trainingExpand
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