Abstract
The Transformer architecture has become the state-of-the-art in Machine Translation. This model, which relies on attention-based mechanisms, has outperformed previous neural machine translation architectures in several tasks. In this system description paper, we report details of training neural machine translation with multi-source Romance languages with the Transformer model and in the evaluation frame of the biomedical WMT 2018 task. Using multi-source languages from the same family allows improvements of over 6 BLEU points.- Anthology ID:
- W18-6449
- 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:
- 667–670
- Language:
- URL:
- https://aclanthology.org/W18-6449
- DOI:
- 10.18653/v1/W18-6449
- Cite (ACL):
- Brian Tubay and Marta R. Costa-jussà. 2018. Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 667–670, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task (Tubay & Costa-jussà, WMT 2018)
- PDF:
- https://preview.aclanthology.org/author-url/W18-6449.pdf