Abstract
This paper describes our submission for the English-Tamil news translation task of WMT-2020. The various techniques and Neural Machine Translation (NMT) models used by our team are presented and discussed, including back-translation, fine-tuning and word dropout. Additionally, our experiments show that using a linguistically motivated subword segmentation technique (Ataman et al., 2017) does not consistently outperform the more widely used, non-linguistically motivated SentencePiece algorithm (Kudo and Richardson, 2018), despite the agglutinative nature of Tamil morphology.- Anthology ID:
- 2020.wmt-1.9
- 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:
- 126–133
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.9
- DOI:
- Cite (ACL):
- Prajit Dhar, Arianna Bisazza, and Gertjan van Noord. 2020. Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen’s Submission to WMT-2020. In Proceedings of the Fifth Conference on Machine Translation, pages 126–133, Online. Association for Computational Linguistics.
- Cite (Informal):
- Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen’s Submission to WMT-2020 (Dhar et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wmt-1.9.pdf