Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages
Atul Kr. Ojha, Valentin Malykh, Alina Karakanta, Chao-Hong Liu
Abstract
This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages. This task was organised as part of the 3rd Workshop on Technologies for MT of Low Resource Languages (LoResMT) at AACL-IJCNLP 2020. The focus was on the zero-shot approach as a notable development in Neural Machine Translation to build MT systems for language pairs where parallel corpora are small or even non-existent. The shared task experience suggests that back-translation and domain adaptation methods result in better accuracy for small-size datasets. We further noted that, although translation between similar languages is no cakewalk, linguistically distinct languages require more data to give better results.- Anthology ID:
- 2020.loresmt-1.4
- Volume:
- Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Alina Karakanta, Atul Kr. Ojha, Chao-Hong Liu, Jade Abbott, John Ortega, Jonathan Washington, Nathaniel Oco, Surafel Melaku Lakew, Tommi A Pirinen, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
- Venue:
- LoResMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 33–37
- Language:
- URL:
- https://aclanthology.org/2020.loresmt-1.4
- DOI:
- Cite (ACL):
- Atul Kr. Ojha, Valentin Malykh, Alina Karakanta, and Chao-Hong Liu. 2020. Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages, pages 33–37, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages (Ojha et al., LoResMT 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.loresmt-1.4.pdf