Research on Transformer-Based Multilingual Machine Translation Methods

Because of the great difference in word order between different languages in machine translation, the translation model has the problem of wrong translation. Translation models with the same target language and different source languages learn different word order information, resulting in different translation quality. Therefore, this paper proposes a multilingual neural machine translation model with multiple languages at the source and one language at the target. Multiple languages with different word orders participate in the model training at the same time, so that the model can learn multiple word order information of sentences with the same meaning. First, 170,000 Russian-Uzbek-Uyghur-English-Chinese parallel corpus is constructed. On this basis, different source languages are added with specified language tags by using the method of adding language tags, and then mixed as a new data set to train a multilingual translation model. In addition, four multilingual neural machine translation models, stacking, parallel, fusion and sublayer fusion, are realized by modifying the Transformer model method. The experimental results show that the method of adding language tags can partially improve the performance of bilingual translation, and the quality of translation can be further improved after the source language is rewritten with Latin letters; The modified four Multilingual models can improve the quality of translation models.