@inproceedings{silva-etal-2024-benchmarking,
title = "Benchmarking Low-Resource Machine Translation Systems",
author = {Silva, Ana and
Srivastava, Nikit and
Moteu Ngoli, Tatiana and
R{\"o}der, Michael and
Moussallem, Diego and
Ngonga Ngomo, Axel-Cyrille},
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.loresmt-1.18",
pages = "175--185",
abstract = "Assessing the performance of machine translation systems is of critical value, especially to languages with lower resource availability.Due to the large evaluation effort required by the translation task, studies often compare new systems against single systems or commercial solutions. Consequently, determining the best-performing system for specific languages is often unclear. This work benchmarks publicly available translation systems across 4 datasets and 26 languages, including low-resource languages. We consider both effectiveness and efficiency in our evaluation.Our results are made public through BENG{---}a FAIR benchmarking platform for Natural Language Generation tasks.",
}
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%0 Conference Proceedings
%T Benchmarking Low-Resource Machine Translation Systems
%A Silva, Ana
%A Srivastava, Nikit
%A Moteu Ngoli, Tatiana
%A Röder, Michael
%A Moussallem, Diego
%A Ngonga Ngomo, Axel-Cyrille
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Abbott, Jade
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Malykh, Valentin
%Y Logacheva, Varvara
%Y Zhao, Xiaobing
%S Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F silva-etal-2024-benchmarking
%X Assessing the performance of machine translation systems is of critical value, especially to languages with lower resource availability.Due to the large evaluation effort required by the translation task, studies often compare new systems against single systems or commercial solutions. Consequently, determining the best-performing system for specific languages is often unclear. This work benchmarks publicly available translation systems across 4 datasets and 26 languages, including low-resource languages. We consider both effectiveness and efficiency in our evaluation.Our results are made public through BENG—a FAIR benchmarking platform for Natural Language Generation tasks.
%U https://aclanthology.org/2024.loresmt-1.18
%P 175-185
Markdown (Informal)
[Benchmarking Low-Resource Machine Translation Systems](https://aclanthology.org/2024.loresmt-1.18) (Silva et al., LoResMT-WS 2024)
ACL
- Ana Silva, Nikit Srivastava, Tatiana Moteu Ngoli, Michael Röder, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. 2024. Benchmarking Low-Resource Machine Translation Systems. In Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024), pages 175–185, Bangkok, Thailand. Association for Computational Linguistics.