Abstract
This report describes YerevaNN’s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en→ru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data.- Anthology ID:
- 2020.wmt-1.88
- 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:
- 820–825
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.88
- DOI:
- Cite (ACL):
- Karen Hambardzumyan, Hovhannes Tamoyan, and Hrant Khachatrian. 2020. YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs. In Proceedings of the Fifth Conference on Machine Translation, pages 820–825, Online. Association for Computational Linguistics.
- Cite (Informal):
- YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs (Hambardzumyan et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wmt-1.88.pdf