Discriminative Syntactic Reranking for Statistical Machine Translation

Simon Carter, Christof Monz


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.
Anthology ID:
2010.amta-papers.1
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
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Publisher:
Association for Machine Translation in the Americas
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Language:
URL:
https://aclanthology.org/2010.amta-papers.1
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PDF:
https://aclanthology.org/2010.amta-papers.1.pdf