arXiv:2606.25365v1 Announce Type: new Abstract: We present a study on low-resource machine translation for the Tangkhul-English (nmf-en) language pair. Tangkhul is a severely under-resourced Tibeto-Burman language spoken primarily in Manipur, India, with virtually no prior natural language processing infrastructure. We describe two systems: (1) a primary system based on ByT5-large fine-tuned on 38,336 Tangkhul-English parallel sentence pairs, and (2) a contrastive system based on mT5-small fine-tuned on the same corpus. Our primary ByT5-large system achieves a corpus BLEU score of 39.97, chrF+
The increasing capability and accessibility of large language models make it possible to address low-resource languages, fostering AI inclusivity and utility.
This research demonstrates progress in bringing AI capabilities to under-resourced languages, which can preserve linguistic diversity and expand AI's global reach, contributing to local data sovereignty efforts.
Machine translation is now shown to be effective for a severely under-resourced language like Tangkhul, opening pathways for broader linguistic inclusion in AI applications.
- · Speakers of low-resource languages
- · AI developers focused on linguistic diversity
- · India (localization of AI capabilities)
- · Monolingual software solutions
- · Organizations ignoring linguistic diversity
Tangkhul speakers gain access to advanced communication tools and information in their native language.
This success could spur similar initiatives for other under-resourced languages, leading to a proliferation of localized AI models.
Increased digital literacy and economic opportunities may emerge in communities whose languages become AI-accessible, potentially influencing geopolitical soft power dynamics.
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