Modeling Discourse Structure for Document-level Neural Machine Translation

Understand

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation.

• Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks.
• In this paper, we propose to improve document-level NMT with the aid of discourse structure information.
• Our encoder is based on a hierarchical attention network (HAN).