@InProceedings{oinam-saharia:2026:wmt,
  author    = {Oinam, Dingku Singh  and  Saharia, Navanath},
  title     = {DELAB-IIITM WMT26: Full Fine-Tuning and LoRA-Based Adaptation for English-to-Low-Resource Indic Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2206--2213},
  abstract  = {This paper describes DELAB-IIITM's submission system for the WMT26 machine translation shared task. We participated in the English-to-low-resource Indic translation direction, covering four language pairs: English to Assamese (Bengali), English to Manipuri (Bengali), English to Manipuri (Meitei Mayek) and English to Bodo (Devanagari). Our fine-tuning process leverages two pre-trained multilingual models: NLLB-200-Distilled-600M (Meta AI) and IndicTrans2-1.1B (AI4Bharat). For NLLB-200, we perform full fine-tuning on English-to-Assamese and English-to-Manipuri (Bengali) directions.nFor IndicTrans2, we employ Low-Rank Adaptation (LoRA) on English-to-Manipuri (Meitei Mayek) and English-to-Bodo directions, reducing trainable parameters from 1.1B to approximately 5.9M. All models are fine-tuned exclusively on the WMT26-provided training data. Our submissions achieved competitive results across all four language pairs. The contrastive systems consistently outperformed the primary systems for Assamese, Manipuri (Bengali) and Bodo, achieving improvements of up to +2.89 BLEU for Bodo and +3.14 BLEU for Manipuri (Bengali). However, an interesting anomaly was observed for Manipuri (Meitei Mayek), where the primary system outperformed the contrastive system despite the latter being subjectively better. We analyze this discrepancy and attribute it to BLEU's sensitivity to surface-level n-gram matching rather than semantic quality.},
  url       = {https://aclanthology.org/2026.wmt-1.159}
}