Domain Adaptive Inference for Neural Machine Translation

Danielle Saunders, Felix Stahlberg, Adrià de Gispert, Bill Byrne


Abstract
We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and report strong improvements across test domains without access to the domain label.
Anthology ID:
P19-1022
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
222–228
Language:
URL:
https://www.aclweb.org/anthology/P19-1022
DOI:
10.18653/v1/P19-1022
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1022.pdf
Video:
 https://vimeo.com/383990540