PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discriminative Syntactic Reranking for Statistical Machine Translation
Simon Carter and Christof Monz
In: AMTA 2010, 2-3 June, 2010, Colorado, USA.

Abstract

This paper describes a method that success- fully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discrimi- native language modelling to rerank the n- best translations generated by a statistical ma- chine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. Whilst parse trees do not consistently help, we show how features extracted from a simple Part-of- Speech annotation layer outperform two com- petitive baselines, leading to significant BLEU improvements on three different test sets.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8084
Deposited By:Christof Monz
Deposited On:17 March 2011