  Previous works have shown that contextual information can improve the
performance of neural machine translation (NMT). However, most existing
document-level NMT methods only consider a few number of previous sentences.
How to make use of the whole document as global contexts is still a challenge.
To address this issue, we hypothesize that a document can be represented as a
graph that connects relevant contexts regardless of their distances. We employ
several types of relations, including adjacency, syntactic dependency, lexical
consistency, and coreference, to construct the document graph. Then, we
incorporate both source and target graphs into the conventional Transformer
architecture with graph convolutional networks. Experiments on various NMT
benchmarks, including IWSLT English--French, Chinese-English, WMT
English--German and Opensubtitle English--Russian, demonstrate that using
document graphs can significantly improve the translation quality. Extensive
analysis verifies that the document graph is beneficial for capturing discourse
phenomena.
