In this paper, we present ANVITA machine translation system submitted to WMT 2026 shared task on Low-Resource Indic Language Translation, where the team participated for six translation directions: {Khasi, Nagamese, and Tagin} ↔ English. Two core ideas shaped the design of ANVITA system. Firstly, transfer learning by symmetric fine-tuning of T5-base model and enhanced cross-lingual transfer from related languages (Assamese to Nagamese) by mitigating script mismatch through transliteration. Secondly, to alleviate data scarcity, primary submissions are trained on datasets augmented with related language corpora and paraphrased pairs (English sentences), utilizing only the organizers provided datasets. For the contrastive submissions, training corpora are further expanded by compiling synthetic parallel data and related language data followed by corpora distillation with suitable selection strategies. ANVITA submissions are evaluated on the official test sets using multiple metrics which include BLEU, METEOR, TER, CHRF++, BERT score, and COMET. In the primary submissions, ANVITA achieved first rank for English→Tagin translation. For the contrastive submissions, ANVITA secured first rank for English → Khasi and Khasi → English with BLEU scores of 32.89 and 26.48 respectively. Furthermore, the contrastive submissions for Nagamese ↔ English and Tagin → English ranked second, underscoring efficacy of the data augmentation strategies applied.

India has a rich diverse linguistic landscape including 22 official languages and 122 major languages. Most of these 122 languages fall into low, extremely low resource categories and pose significant challenges in building robust machine translation system. This paper presents ANVITA Indic LR machine translation system submitted to WMT 2025 shared task on Low-Resource Indic Language Translation covering three extremely low-resource Indian languages Nyshi, Khasi, and Kokborok. A transfer learning based strategy is adopted and selected suitable public pretrained models (NLLB, ByT5), considering aspects such as language, script, tokenization and fine-tuned with the organizer provided dataset. Further, to tackle low-resource language menace better, the pretrained models are enriched with new vocabulary for improved representation of these three languages and selectively augmented data with related-language corpora, supplied by the organizer. The contrastive submissions however made use of supplementary corpora sourced from the web, generated synthetically, and drawn from proprietary data. On the WMT 2025 official test set, ANVITA achieved BLEU score of 2.41-11.59 with 2.2K to 60K corpora and 6.99-19.43 BLUE scores with augmented corpora. Overall ANVITA ranked first for {Nyishi, Kokborok}↔English and second for Khasi↔English across evaluation metrics including BLUE, METEOR, ROUGE-L, chrF and TER.