The Impact of Vocabulary Overlaps on Knowledge Transfer in Multilingual Machine Translation

This paper systematically investigates the impact of joint and disjoint vocabularies on knowledge transfer in multilingual neural machine translation (MNMT). Experiments show that extensive vocabulary overlaps, language relatedness, and domain-match lead to better performance, even in out-of-domain setups.