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.
@article{arxiv.1910.00294,
title = {When and Why is Document-level Context Useful in Neural Machine Translation?},
author = {Yunsu Kim and Duc Thanh Tran and Hermann Ney},
journal= {arXiv preprint arXiv:1910.00294},
year = {2019}
}