One Type Context Is Not Enough: Global Context-aware Neural Machine Translation

Abstract

How to effectively model global context has been a critical challenge for document-level neural machine translation (NMT). Both preceding and global context have been carefully explored in the sequence-to-sequence (seq2seq) framework. However, previous studies generally map global context into one vector, which is not enough to well represent the entire document since this largely ignores the hierarchy between sentences and words within. In this article, we propose to model global context for source language from both sentence level and word level. Specifically at sentence level, we extract useful global context for the current sentence, while at word level, we compute global context against words within the current sentence. On this basis, both kinds of global context can be appropriately fused before being incorporated into the state-of-the-art seq2seq model, i.e., Transformer . Detailed experimentation on various document-level translation tasks shows that global context at both sentence level and word level significantly improve translation performance. More encouraging, both kinds of global context are complementary. This leads to more improvement when both kinds of global context are used.