@inproceedings{ojha-etal-2020-findings,
title = "Findings of the {L}o{R}es{MT} 2020 Shared Task on Zero-Shot for Low-Resource languages",
author = "Ojha, Atul Kr. and
Malykh, Valentin and
Karakanta, Alina and
Liu, Chao-Hong",
editor = "Karakanta, Alina and
Ojha, Atul Kr. and
Liu, Chao-Hong and
Abbott, Jade and
Ortega, John and
Washington, Jonathan and
Oco, Nathaniel and
Lakew, Surafel Melaku and
Pirinen, Tommi A and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.loresmt-1.4/",
doi = "10.18653/v1/2020.loresmt-1.4",
pages = "33--37",
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."
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%0 Conference Proceedings
%T Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages
%A Ojha, Atul Kr.
%A Malykh, Valentin
%A Karakanta, Alina
%A Liu, Chao-Hong
%Y Karakanta, Alina
%Y Ojha, Atul Kr.
%Y Liu, Chao-Hong
%Y Abbott, Jade
%Y Ortega, John
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Lakew, Surafel Melaku
%Y Pirinen, Tommi A.
%Y Malykh, Valentin
%Y Logacheva, Varvara
%Y Zhao, Xiaobing
%S Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F ojha-etal-2020-findings
%X 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.
%R 10.18653/v1/2020.loresmt-1.4
%U https://aclanthology.org/2020.loresmt-1.4/
%U https://doi.org/10.18653/v1/2020.loresmt-1.4
%P 33-37
Markdown (Informal)
[Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages](https://aclanthology.org/2020.loresmt-1.4/) (Ojha et al., LoResMT 2020)
ACL