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arXiv · 1906.00408

Domain Adaptive Inference for Neural Machine Translation

Danielle Saunders·Felix Stahlberg·Adria de Gispert·Bill Byrne
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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 show strong improvements across test domains without access to the domain label.

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Danielle Saunders, Felix Stahlberg, Adria de Gispert, Bill Byrne. 2019-06-02. Domain Adaptive Inference for Neural Machine Translation. https://arxiv.org/abs/1906.00408

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