Machine Translation

, Volume 25, Issue 4, pp 317–339

Syntactic discriminative language model rerankers for statistical machine translation

Open Access
Article

DOI: 10.1007/s10590-011-9108-7

Cite this article as:
Carter, S. & Monz, C. Machine Translation (2011) 25: 317. doi:10.1007/s10590-011-9108-7

Abstract

This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical Machine Translation output and human translations. 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. While deep features extracted from parse trees do not consistently help, we show how features extracted from a shallow Part-of-Speech annotation layer outperform a competitive baseline and a state-of-the-art comparative reranking approach, leading to significant BLEU improvements on three different test sets.

Keywords

Statistical machine translation Discriminative language models Syntax 
Download to read the full article text

Copyright information

© The Author(s) 2011

Authors and Affiliations

  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations