When and Why is Document-level Context Useful in Neural Machine Translation?

Yunsu Kim, Duc Thanh Tran, Hermann Ney


Abstract
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.
Anthology ID:
D19-6503
Volume:
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
DiscoMT | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–34
Language:
URL:
https://www.aclweb.org/anthology/D19-6503
DOI:
10.18653/v1/D19-6503
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