Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation
Ekaterina Garmash, Christof Monz · 2014
This paper presents a novel approach to improve reordering in phrase-based ma-chine translation by using richer, syntac-tic 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 demon-strate significant improvements in BLEU and TER over the phrase-based baseline, as well as over the lexicalized BiLM by Niehues et al. (2011). Further improve-ments of up to 0.45 BLEU for Arabic-English and up to 0.59 BLEU for Chinese-English are obtained by combining our de-pendency BiLM with a lexicalized BiLM. An improvement of 0.98 BLEU is ob-tained for Chinese-English in the setting of an increased distortion limit. 1