Multilingual Neural Machine Translation with Language Clustering
Table 3: BLEU score of English→23 languages with multilingual models based on different methods of language clustering: Universal (all the languages share one model), Individual (each language with separate model), Family (Language Family), Embedding (Language Embedding).
['Language', 'Ar', 'Bg', 'Cs', 'De', 'El', 'Es', 'Fa', 'Fr', 'He', 'Hu', 'It', 'Ja']
We also have several findings: These findings show that language embeddings can stably characterize relationships among languages no matter in one-to-many setting or in many-to-one setting. Although there exist some differences in the clustering results between one-to-many setting and many-to-one setting due to the nature of the learning based clustering method, most of the clustering results are reasonable. However, there are still several languages on which our clustering methods (Family or Embedding) largely outperform Individual. The reason is similar as the many-to-one setting that some languages are of smaller data sizes. In this situation, similar languages clustered together will boost the accuracy compared with separate model.