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

@inproceedings{Sudoh2010DivideAT,
  title={Divide and Translate: Improving Long Distance Reordering in Statistical Machine Translation},
  author={Katsuhito Sudoh and Kevin Duh and Hajime Tsukada and Tsutomu Hirao and Masaaki Nagata},
  booktitle={WMT@ACL},
  year={2010}
}
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 reconstructs the target sentence using the clause translations with non-terminals. The nonterminals are placeholders of embedded clauses, by which we reduce complicated clause-level reordering into simple wordlevel reordering. Its translation model is trained using a bilingual corpus with clause-level… CONTINUE READING
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  • We achieved significant 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 research paper abstracts in the medical domain.

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