PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Translating with non-contiguous phrases
Michel Simard, Nicola Cancedda, Bruno Cavestro, Marc Dymetman, Eric Gaussier, Cyril Goutte, Kenji Yamada, Philippe Langlais and Arne Mauser
In: HLT/EMNLP 2005, 6-8 Oct 2005, Vancouver, BC, Canada.

Abstract

This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. A method for producing such phrases from a word-aligned corpora is proposed. A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. Translations are produced by means of a beam-search decoder. Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Theory & Algorithms
ID Code:1114
Deposited By:Nicola Cancedda
Deposited On:05 October 2005