Improved Neural Machine Translation using Side Information

Cong Duy Vu Hoang, Gholamreza Haffari, Trevor Cohn


Abstract
In this work, we investigate whether side information is helpful in neural machine translation (NMT). We study various kinds of side information, including topical information, personal trait, then propose different ways of incorporating them into the existing NMT models. Our experimental results show the benefits of side information in improving the NMT models.
Anthology ID:
U18-1001
Volume:
Proceedings of the Australasian Language Technology Association Workshop 2018
Month:
December
Year:
2018
Address:
Dunedin, New Zealand
Venue:
ALTA
SIG:
Publisher:
Note:
Pages:
6–16
Language:
URL:
https://www.aclweb.org/anthology/U18-1001
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/U18-1001.pdf