Pivot language approach for phrase-based statistical machine translation

摘要

This paper proposes a novel method for phrase-based statistical machine translation based on the use of a pivot language. To translate between languages L_s and L_t with limited bilingual resources, we bring in a third language, L_p, called the pivot language. For the language pairs L_s - L_p and L_p - L_t, there exist large bilingual corpora. Using only L_s - L_p and L_p - L_t bilingual corpora, we can build a translation model for L_s - L_t. The advantage of this method lies in the fact that we can perform translation between L_s and L_t even if there is no bilingual corpus available for this language pair. Using BLEU as a metric, our pivot language approach significantly outperforms the standard model trained on a small bilingual corpus. Moreover, with a small L_s - L_t bilingual corpus available, our method can further improve translation quality by using the additional L_s - L_p and L_p - L_t bilingual corpora.