Domain Adaptive Inference for Neural Machine Translation
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Peer-reviewed
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Saunders, Danielle https://orcid.org/0000-0002-7943-3102
Stahlberg, Felix https://orcid.org/0000-0002-0430-5704
Gispert, Adria de
Byrne, Bill
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 show strong improvements across test domains without access to the domain label.
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cs.CL, cs.CL
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Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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All rights reserved