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://preview.aclanthology.org/remove-affiliations/2020.loresmt-1.4/
DOI:
10.18653/v1/2020.loresmt-1.4
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/remove-affiliations/2020.loresmt-1.4.pdf
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