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    <abstract>This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine 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 competitive baselines, leading to significant BLEU improvements on three different test sets.</abstract>
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%0 Conference Proceedings
%T Discriminative Syntactic Reranking for Statistical Machine Translation
%A Carter, Simon
%A Monz, Christof
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F carter-monz-2010-discriminative
%X This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine 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 competitive baselines, leading to significant BLEU improvements on three different test sets.
%U https://aclanthology.org/2010.amta-papers.1

Markdown (Informal)

[Discriminative Syntactic Reranking for Statistical Machine Translation](https://aclanthology.org/2010.amta-papers.1) (Carter & Monz, AMTA 2010)

• Discriminative Syntactic Reranking for Statistical Machine Translation (Carter & Monz, AMTA 2010)

ACL

• Simon Carter and Christof Monz. 2010. Discriminative Syntactic Reranking for Statistical Machine Translation. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.