Bilingual structured language models for statistical machine translation

Abstract

This paper describes a novel target-side syntactic language model for phrase-based statistical machine translation, bilingual structured language model. Our approach represents a new way to adapt structured language models (Chelba and Jelinek, 2000) to statistical machine translation, and a first attempt to adapt them to phrasebased statistical machine translation. We propose a number of variations of the bilingual structured language model and evaluate them in a series of rescoring experiments. Rescoring of 1000-best translation lists produces statistically significant improvements of up to 0.7 BLEU over a strong baseline for Chinese-English, but does not yield improvements for Arabic-English.