Abstract
This paper describes the joint participation of University of Helsinki and Aalto University to two shared tasks of WMT 2020: the news translation between Inuktitut and English and the low-resource translation between German and Upper Sorbian. For both tasks, our efforts concentrate on efficient use of monolingual and related bilingual corpora with scheduled multi-task learning as well as an optimized subword segmentation with sampling. Our submission obtained the highest score for Upper Sorbian -> German and was ranked second for German -> Upper Sorbian according to BLEU scores. For English–Inuktitut, we reached ranks 8 and 10 out of 11 according to BLEU scores.- Anthology ID:
- 2020.wmt-1.134
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1129–1138
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.134
- DOI:
- Cite (ACL):
- Yves Scherrer, Stig-Arne Grönroos, and Sami Virpioja. 2020. The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks. In Proceedings of the Fifth Conference on Machine Translation, pages 1129–1138, Online. Association for Computational Linguistics.
- Cite (Informal):
- The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks (Scherrer et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wmt-1.134.pdf