When and Why is Document-level Context Useful in Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney · 2019
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences.However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets.We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT.We show that most of the improvements are not interpretable as utilizing the context.We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.