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    <abstract>While the quality of machine translation (MT) between widely-spoken languages has improved dramatically in recent years, training robust MT systems for languages with fewer resources remains a challenge. Endangered languages, which often lack the speaker population and written tradition needed to create text resources, are at a particular disadvantage. Developing robust MT architectures for very low-resource settings is hampered by the lack of suitable parallel corpora. To address this challenge, we introduce FormosanMT, a set of MT-ready parallel corpora for the Formosan family of endangered languages indigenous to Taiwan. Together the corpora total nearly 500,000 Formosan-Mandarin and Formosan-English sentence pairs. We share scripts for extracting these corpora from public sources, along with customizable tools for filtering, normalizing, and partitioning the data. In addition, we provide a new tokenizer for Traditional Chinese writing compatible with the popular No Language Left Behind (NLLB) MT architecture, along with updated and improved code for fine-tuning NLLB for any low-resource language pair. Finally we distribute our fully trained NLLB and OpenNMT models for the Formosan languages to and from both Mandarin and English. In addition to serving as a valuable resource for the Formosan language speaker communities, our data, code, and models will be available to NLP researchers working on endangered and low-resource language MT.</abstract>
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%0 Conference Proceedings
%T FormosanMT: A Multilingual Parallel Corpus of the Formosan Language Family
%A Scheppat, Hunter
%A Hartshorne, Joshua K.
%A Koc, Sema
%A Le Ferrand, Éric
%A Prud’hommeaux, Emily
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F scheppat-etal-2026-formosanmt
%X While the quality of machine translation (MT) between widely-spoken languages has improved dramatically in recent years, training robust MT systems for languages with fewer resources remains a challenge. Endangered languages, which often lack the speaker population and written tradition needed to create text resources, are at a particular disadvantage. Developing robust MT architectures for very low-resource settings is hampered by the lack of suitable parallel corpora. To address this challenge, we introduce FormosanMT, a set of MT-ready parallel corpora for the Formosan family of endangered languages indigenous to Taiwan. Together the corpora total nearly 500,000 Formosan-Mandarin and Formosan-English sentence pairs. We share scripts for extracting these corpora from public sources, along with customizable tools for filtering, normalizing, and partitioning the data. In addition, we provide a new tokenizer for Traditional Chinese writing compatible with the popular No Language Left Behind (NLLB) MT architecture, along with updated and improved code for fine-tuning NLLB for any low-resource language pair. Finally we distribute our fully trained NLLB and OpenNMT models for the Formosan languages to and from both Mandarin and English. In addition to serving as a valuable resource for the Formosan language speaker communities, our data, code, and models will be available to NLP researchers working on endangered and low-resource language MT.
%R 10.63317/3ut33xim33nt
%U https://aclanthology.org/2026.lrec-1.273/
%U https://doi.org/10.63317/3ut33xim33nt
%P 3445-3455

Markdown (Informal)

[FormosanMT: A Multilingual Parallel Corpus of the Formosan Language Family](https://aclanthology.org/2026.lrec-1.273/) (Scheppat et al., LREC 2026)

• FormosanMT: A Multilingual Parallel Corpus of the Formosan Language Family (Scheppat et al., LREC 2026)

ACL

• Hunter Scheppat, Joshua K. Hartshorne, Sema Koc, Éric Le Ferrand, and Emily Prud’hommeaux. 2026. FormosanMT: A Multilingual Parallel Corpus of the Formosan Language Family. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 3445–3455, Palma de Mallorca, Spain. ELRA Language Resource Association.