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        <title>SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation</title>
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        <namePart type="given">Sanjeev</namePart>
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        <namePart type="given">Preethi</namePart>
        <namePart type="family">Jyothi</namePart>
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    <abstract>Multilingual models are widely used for machine translation (MT). However, their effectiveness for extremely low-resource languages (ELRLs) depends critically on how related languages are incorporated during fine-tuning. In this work, we study the role of language mixing directionality, linguistic relatedness, and script compatibility in ELRL translation. We propose SrcMix, a simple source-side mixing strategy that combines related ELRLs during fine-tuning while constraining the decoder to a single target language. Compared to its target-side counterpart TgtMix, SrcMix improves performance by +3 ChrF++ and +5 BLEU in high-resource to ELRL translations, and by +5 ChrF++ and +12 BLEU in mid-resource to ELRL translations. We also release the first Angika MT dataset and provide a systematic comparison of LLM (Aya-101) and NMT (mT5-Large) models under ELRL settings, highlighting the importance of directional mixing and linguistic compatibility.</abstract>
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%0 Conference Proceedings
%T SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation
%A Kumar, Sanjeev
%A Jyothi, Preethi
%A Bhattacharyya, Pushpak
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F kumar-etal-2026-srcmix
%X Multilingual models are widely used for machine translation (MT). However, their effectiveness for extremely low-resource languages (ELRLs) depends critically on how related languages are incorporated during fine-tuning. In this work, we study the role of language mixing directionality, linguistic relatedness, and script compatibility in ELRL translation. We propose SrcMix, a simple source-side mixing strategy that combines related ELRLs during fine-tuning while constraining the decoder to a single target language. Compared to its target-side counterpart TgtMix, SrcMix improves performance by +3 ChrF++ and +5 BLEU in high-resource to ELRL translations, and by +5 ChrF++ and +12 BLEU in mid-resource to ELRL translations. We also release the first Angika MT dataset and provide a systematic comparison of LLM (Aya-101) and NMT (mT5-Large) models under ELRL settings, highlighting the importance of directional mixing and linguistic compatibility.
%U https://aclanthology.org/2026.findings-eacl.332/
%P 6306-6323

Markdown (Informal)

[SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation](https://aclanthology.org/2026.findings-eacl.332/) (Kumar et al., Findings 2026)

• SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation (Kumar et al., Findings 2026)

ACL

• Sanjeev Kumar, Preethi Jyothi, and Pushpak Bhattacharyya. 2026. SrcMix: Mixing of Related Source Languages Benefits Extremely Low-resource Machine Translation. In Findings of the Association for Computational Linguistics: EACL 2026, pages 6306–6323, Rabat, Morocco. Association for Computational Linguistics.