Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation

This paper presents a novel approach to improve reordering in phrase-based machine translation by using richer, syntactic representations of units of bilingual language models (BiLMs). Our method to include syntactic information is simple in implementation and requires minimal changes in the decoding algorithm. The approach is evaluated in a series of Arabic-English and Chinese-English translation experiments. The best models demonstrate significant improvements in BLEU and TER over the phrase-based baseline, as well as over the lexicalized BiLM by Niehues et al. (2011). Further improvments of up to 0.45 BLEU for Arabic-English and up to 0.59 BLEU for Chinese-English are obtained by combining our dependency BiLM with a lexicalized BiLM. An improvement of 0.98 BLEU is obtained for Chinese-English in the setting of an increased distortion limit.