Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation

Understand

Using a vocabulary that is shared across languages is common practice in Multilingual Neural Machine Translation (MNMT).

• In addition to its simple design, shared tokens play an important role in positive knowledge transfer, assuming that shared tokens refer to similar meanings across languages.
• However, when word overlap is small, especially due to different writing systems, transfer is inhibited.
• In this paper, we define word-level information transfer pathways via word equivalence classes and rely on graph networks to fuse word embeddings across languages.