@inproceedings{mosolova-smaili-2022-chance,
title = "The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia",
author = "Mosolova, Anna and
Smaili, Kamel",
editor = "Ojha, Atul Kr. and
Liu, Chao-Hong and
Vylomova, Ekaterina and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Pirinen, Tommi A and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.loresmt-1.4",
pages = "23--34",
abstract = "Numerous machine translation systems have been proposed since the appearance of this task. Nowadays, new large language model-based algorithms show results that sometimes overcome human ones on the rich-resource languages. Nevertheless, it is still not the case for the low-resource languages, for which all these algorithms did not show equally impressive results. In this work, we want to compare 3 generations of machine translation models on 7 low-resource languages and make a step further by proposing a new way of automatic parallel data augmentation using the state-of-the-art generative model.",
}
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%0 Conference Proceedings
%T The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia
%A Mosolova, Anna
%A Smaili, Kamel
%Y Ojha, Atul Kr.
%Y Liu, Chao-Hong
%Y Vylomova, Ekaterina
%Y Abbott, Jade
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Pirinen, Tommi A.
%Y Malykh, Valentin
%Y Logacheva, Varvara
%Y Zhao, Xiaobing
%S Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F mosolova-smaili-2022-chance
%X Numerous machine translation systems have been proposed since the appearance of this task. Nowadays, new large language model-based algorithms show results that sometimes overcome human ones on the rich-resource languages. Nevertheless, it is still not the case for the low-resource languages, for which all these algorithms did not show equally impressive results. In this work, we want to compare 3 generations of machine translation models on 7 low-resource languages and make a step further by proposing a new way of automatic parallel data augmentation using the state-of-the-art generative model.
%U https://aclanthology.org/2022.loresmt-1.4
%P 23-34
Markdown (Informal)
[The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia](https://aclanthology.org/2022.loresmt-1.4) (Mosolova & Smaili, LoResMT 2022)
ACL