Pivot Language Approach for Phrase-Based Statistical Machine Translation

This paper proposes a novel method for phrase-based statistical machine translation by using pivot language. To conduct translation between languages Lf and Le with a small bilingual corpus, we bring in a third language Lp, which is named the pivot language. For Lf-Lp and Lp-Le, there exist large bilingual corpora. Using only Lf-Lp and Lp-Le bilingual corpora, we can build a translation model for Lf-Le. The advantage of this method lies in that we can perform translation between Lf and Le even if there is no bilingual corpus available for this language pair. Using BLEU as a metric, our pivot language method achieves an absolute improvement of 0.06 (22.13% relative) as compared with the model directly trained with 5,000 Lf-Le sentence pairs for French-Spanish translation. Moreover, with a small Lf-Le bilingual corpus available, our method can further improve the translation quality by using the additional Lf-Lp and Lp-Le bilingual corpora.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Hua Wu, Haifeng Wang
• Keywords: ACL 2007, Bilingual Corpora, Bilingual Corpus, Computational Linguistics, Pivot Language