Neural Machine Translation for Low-Resource Tangkhul--English
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Computer Science > Computation and Language
Title:Neural Machine Translation for Low-Resource Tangkhul--English
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++ of 58.07, BERTScore F1 of 0.8104, and COMET (wmt22-comet-da) of 0.7302 on a held-out test set of 3,856 sentences. We further discuss the orthographic challenges specific to Tangkhul's Latin-script diacritics, the domain bias of our training corpus (which comprises biblical text, stories, and conversational data), and avenues for future improvement through data diversification and domain adaptation.
| Comments: | 11 pages, 3 figures, 9 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.25365 [cs.CL] |
| (or arXiv:2606.25365v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25365
arXiv-issued DOI via DataCite (pending registration)
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