This paper describes our submission to the WMT 2026 Low-Resource Indic Language Translation Shared Task for the English-Assamese and English-Bodo pairs in both translation directions. We fine-tune the pretrained NLLB-200 distilled 600M model for primary as well as contrastive tasks. Since Bodo is not supported by NLLB-200, we use the Hindi (hin_Deva) language tag as a proxy for Bodo during fine-tuning and inference, based on an empirical evaluation of Devanagari-script language tags. The constrained systems are trained exclusively on the official task data, while the contrastive systems additionally incorporate publicly available parallel corpora, including AI4Bharat BPCC and Google Smol. A rigorous preprocessing pipeline, combined with the extra parallel data, yields consistent gains across all automatic metrics, with the largest improvements observed for the lower-resource English-Bodo pair.

Modern Translation Systems heavily rely on high-quality, large parallel datasets for state-of-the-art performance. However, such resources are largely unavailable for most of the South Asian languages. Among them, Nepali and Tamang fall into such category, with Tamang being among the least digitally resourced languages in the region. This work addresses the gap by developing NepTam20K, a 20K gold standard parallel corpus, and NepTam80K, an 80K synthetic Nepali–Tamang parallel corpus, both sentence-aligned and designed to support machine translation. The datasets were created through a pipeline involving data scraping from Nepali news and online sources, pre-processing, semantic filtering, balancing for tense and polarity (in NepTam20K dataset), expert translation into Tamang by native speakers of the language, and verification by an expert Tamang linguist. The dataset covers five domains: Agriculture, Health, Education and Technology, Culture, and General Communication. To evaluate the dataset, baseline machine translation experiments were carried out using various multilingual pre-trained models:mBART, M2M-100, NLLB-200, and a vanilla Transformer model. The fine-tuning on the NLLB-200 achieved the highest sacreBLEU scores of 40.92 (Nepali → Tamang) and 45.26 (Tamang → Nepali).