@inproceedings{saunders-etal-2019-domain,
title = "Domain Adaptive Inference for Neural Machine Translation",
author = "Saunders, Danielle and
Stahlberg, Felix and
de Gispert, Adri{\`a} and
Byrne, Bill",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1022",
doi = "10.18653/v1/P19-1022",
pages = "222--228",
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.",
}
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%0 Conference Proceedings
%T Domain Adaptive Inference for Neural Machine Translation
%A Saunders, Danielle
%A Stahlberg, Felix
%A de Gispert, Adrià
%A Byrne, Bill
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F saunders-etal-2019-domain
%X 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.
%R 10.18653/v1/P19-1022
%U https://aclanthology.org/P19-1022
%U https://doi.org/10.18653/v1/P19-1022
%P 222-228
Markdown (Informal)
[Domain Adaptive Inference for Neural Machine Translation](https://aclanthology.org/P19-1022) (Saunders et al., ACL 2019)
ACL
- Danielle Saunders, Felix Stahlberg, Adrià de Gispert, and Bill Byrne. 2019. Domain Adaptive Inference for Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 222–228, Florence, Italy. Association for Computational Linguistics.