Research on system combination of machine translation based on Transformer

Influenced by its training corpus, the performance of different machine translation systems varies greatly. Aiming at achieving higher quality translations, system combination methods combine the translation results of multiple systems through statistical combination or neural network combination. This paper proposes a new multi-system translation combination method based on the Transformer architecture, which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder combination. The experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU) points compared with the best single system results, 0.71-3.12more BLEU points compared with the statistical combination method, and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination method. The experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.