When And Why Is Document-level Context Useful In Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney . Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019) 2019 – 68 citations
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.