Abstract
The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self-attention-based neural models on German-English, English-German, and Chinese-English. Our final system combines all neural models together with a phrase-based SMT system in an MBR-based scheme. We report small but consistent gains on top of strong Transformer ensembles.- Anthology ID:
- W18-6427
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 504–512
- Language:
- URL:
- https://aclanthology.org/W18-6427
- DOI:
- 10.18653/v1/W18-6427
- Cite (ACL):
- Felix Stahlberg, Adrià de Gispert, and Bill Byrne. 2018. The University of Cambridge’s Machine Translation Systems for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 504–512, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The University of Cambridge’s Machine Translation Systems for WMT18 (Stahlberg et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6427.pdf