@InProceedings{maiti-muley-sahoo:2026:wmt1,
  author    = {Maiti, Agniva  and  Muley, Aarsh  and  Sahoo, Sovan Kumar},
  title     = {SCE-KIIT: Fine-Tuning NLLB-200 for English-Nagamese Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2180--2189},
  abstract  = {Nagamese is a critically under-resourced Assamese-based creole language spoken in Nagaland, India, serving as the primary lingua franca for over two million speakers across 16 Naga tribal communities. Written in the Latin script, it is entirely absent from all modern massively multilingual machine translation (MT) systems. We describe the SCE-KIIT submission to the WMT 2026 Shared Task on Low-Resource Indic Language Translation (Category 2: English-Nagamese), for which we submitted a primary EN-NAG run. Our system builds on Facebook's NLLB-200 distilled with 600M parameters using a two-phase surrogate-token strategy: we fine-tune with Assamese (asm\_Beng) as the target-side language prefix, then force asm\_Latn at inference time. This token is absent from NLLB's vocabulary and resolves to the unknown token (UNK); we hypothesise that seeding generation with an out-of-vocabulary token leaves the decoder's script preference unconstrained, allowing the Romanised output learned during fine-tuning to surface. On our 228-sentence held-out test split, the system achieves SacreBLEU 40.02, chrF 53.61, and COMET 0.7212 (wmt22-comet-da), against a zero-shot baseline of 0.40 BLEU and 0.4487 COMET: an absolute gain of 39.62 BLEU. On the official shared-task test set, which is out-of-domain news text, the same system scores 12.51 BLEU and 43.81 chrF++; we analyse this domain gap in detail. The model and corpus are publicly released at https://huggingface.co/agnivamaiti/nllb-200-en-nagamese.},
  url       = {https://aclanthology.org/2026.wmt-1.156}
}