%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