@InProceedings{salvin-EtAl:2026:wmt,
  author    = {Salvin, G.L. John  and  Ramesan, Amisha  and  Chigwededza, Abigairl Nyasha  and  Budde, Shrikant Tryambak  and  Hingmire, Swapnil},
  title     = {DoDS-IITPKD: LoRA Fine-Tuning and LLM Post-Editing for Low-Resource Indic Machine Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {614--619},
  abstract  = {We describe the DoDS-IITPKD submissions to the WMT 2026 Shared Task on Low-Resource Indic Language Translation. We cover six English-centric pairs in both directions: Assamese, Mizo, Khasi, and Manipuri in Category 1, and Bodo and Kokborok in Category 2. Each system adapts a frozen multilingual backbone with low-rank adapters (LoRA with DoRA and rank-stabilised scaling). NLLB-200-3.3B handles Mizo, Khasi, and Kokborok, and IndicTrans2-1B handles Assamese, Manipuri, and Bodo. For the two languages outside the NLLB tokenizer (Khasi and Kokborok) we use same-script surrogate language tags. We add self back-translation for the Indic-English directions and, for some systems, a line-aligned LLM post-editing pass. On the official test sets, our systems obtain the best BLEU of any submission for Bodo to English (36.10) and English to Kokborok (7.36), and the best on time BLEU for Kokborok to English (20.28). LLM post-editing gives large gains in the Indic-English direction (up to +8.20 BLEU) and almost none in the English-Indic direction.},
  url       = {https://aclanthology.org/2026.wmt-1.34}
}