Divide and Translate: Improving Long Distance Reordering in Statistical Machine Translation

This paper proposes a novel method for long distance, clause-level reordering in statistical machine translation (SMT). The proposed method separately translates clauses in the source sentence and recon-structs the target sentence using the clause translations with non-terminals. The non-terminals are placeholders of embedded clauses, by which we reduce complicated clause-level reordering into simple word-level reordering. Its translation model is trained using a bilingual corpus with clause-level alignment, which can be au-tomatically annotated by our alignment algorithm with a syntactic parser in the source language. We achieved signifi-cant improvements of 1.4 % in BLEU and 1.3 % in TER by using Moses, and 2.2% in BLEU and 3.5 % in TER by using our hierarchical phrase-based SMT, for the English-to-Japanese translation of re-search paper abstracts in the medical do-main.