Domain Adaptive Inference for Neural Machine Translation
Danielle Saunders, Felix Stahlberg, Adrià de Gispert, Bill Byrne
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.- Anthology ID:
- P19-1022
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 222–228
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/P19-1022/
- DOI:
- 10.18653/v1/P19-1022
- Cite (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.
- Cite (Informal):
- Domain Adaptive Inference for Neural Machine Translation (Saunders et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/P19-1022.pdf