In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.
@cite_5: We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of general translation quality and pronoun prediction, when trained on small corpora, although this improvement largely disappears when trained with a larger corpus. We also discover that attention-based neural machine translation is well suited for pronoun prediction and compares favorably with other approaches that were specifically designed for this task.
While both our approach and use Auxiliary Context mechanism for incorporating cross-sentence context, there are two main differences: 1) we have separate parameters to better control the effects of the cross- and intra-sentence contexts, while they only have one parameter matrix to manage the single representation that encodes both contexts; 2) based on the intuition that not every target word generation requires equivalent cross-sentence context, we introduce a context gate @cite_5 to control the amount of information from it, while they don't.