Incorporating Typological Features into Language Selection for Multilingual Neural Machine Translation

In this paper, we propose to use rich semantic and typological information of languages to improve the language selection method for multilingual NMT. In particular, we first use a graph-based model to output the most semantic similarity languages; then, a random forest model is built which integrates features such as data size, language family, word formation, morpheme overlap, word order, POS tag and syntax similarity together to predict the final target language(s). Experimental results on several datasets show that our method achieves consistent improvements over existing approaches both on language selection and multilingual NMT.