Pivot language approach for phrase-based statistical machine translation

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.