Abstract
The 2019 WMT Biomedical translation task involved translating Medline abstracts. We approached this using transfer learning to obtain a series of strong neural models on distinct domains, and combining them into multi-domain ensembles. We further experimented with an adaptive language-model ensemble weighting scheme. Our submission achieved the best submitted results on both directions of English-Spanish.- Anthology ID:
- W19-5421
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 169–174
- Language:
- URL:
- https://aclanthology.org/W19-5421
- DOI:
- 10.18653/v1/W19-5421
- Cite (ACL):
- Danielle Saunders, Felix Stahlberg, and Bill Byrne. 2019. UCAM Biomedical Translation at WMT19: Transfer Learning Multi-domain Ensembles. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 169–174, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- UCAM Biomedical Translation at WMT19: Transfer Learning Multi-domain Ensembles (Saunders et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W19-5421.pdf