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            <title>Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024</title>
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    <abstract>Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these languages, if such corpora are available at all. NLP in Ethiopian languages suffers from the same issues due to the unavailability of publicly accessible datasets for NLP tasks, including MT. To help the research community and foster research for Ethiopian languages, we introduce EthioMT – a new parallel corpus for 15 languages. We also create a new benchmark by collecting a dataset for better-researched languages in Ethiopia. We evaluate the newly collected corpus and the benchmark dataset for 23 Ethiopian languages using transformer and fine-tuning approaches.</abstract>
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%0 Conference Proceedings
%T EthioMT: Parallel Corpus for Low-resource Ethiopian Languages
%A Tonja, Atnafu Lambebo
%A Kolesnikova, Olga
%A Gelbukh, Alexander
%A Kalita, Jugal
%Y Mabuya, Rooweither
%Y Matfunjwa, Muzi
%Y Setaka, Mmasibidi
%Y van Zaanen, Menno
%S Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tonja-etal-2024-ethiomt
%X Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these languages, if such corpora are available at all. NLP in Ethiopian languages suffers from the same issues due to the unavailability of publicly accessible datasets for NLP tasks, including MT. To help the research community and foster research for Ethiopian languages, we introduce EthioMT – a new parallel corpus for 15 languages. We also create a new benchmark by collecting a dataset for better-researched languages in Ethiopia. We evaluate the newly collected corpus and the benchmark dataset for 23 Ethiopian languages using transformer and fine-tuning approaches.
%U https://aclanthology.org/2024.rail-1.12
%P 107-114

Markdown (Informal)

[EthioMT: Parallel Corpus for Low-resource Ethiopian Languages](https://aclanthology.org/2024.rail-1.12) (Tonja et al., RAIL-WS 2024)

• EthioMT: Parallel Corpus for Low-resource Ethiopian Languages (Tonja et al., RAIL-WS 2024)

ACL

• Atnafu Lambebo Tonja, Olga Kolesnikova, Alexander Gelbukh, and Jugal Kalita. 2024. EthioMT: Parallel Corpus for Low-resource Ethiopian Languages. In Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024, pages 107–114, Torino, Italia. ELRA and ICCL.