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.
@article{arxiv.1906.00408,
title = {Domain Adaptive Inference for Neural Machine Translation},
author = {Danielle Saunders and Felix Stahlberg and Adria de Gispert and Bill Byrne},
journal= {arXiv preprint arXiv:1906.00408},
year = {2019}
}