  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). Specifically, we first parse the input document to obtain its discourse
structure. Then, we introduce a Transformer-based path encoder to embed the
discourse structure information of each word. Finally, we combine the discourse
structure information with the word embedding before it is fed into the
encoder. Experimental results on the English-to-German dataset show that our
model can significantly outperform both Transformer and Transformer+HAN.
