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
- 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/auto-file-uploads/W19-5421.pdf