Domain Adaptive Inference for Neural Machine Translation

ACL 2019

Domain Adaptive Inference for Neural Machine Translation

Jan 30, 2021
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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. Authors: Danielle Saunders, Felix Stahlberg, Adrià de Gispert, Bill Byrne (University of Cambridge)

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