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    <abstract>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} \ensuremathłeftrightarrow 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 \ensuremathłeftrightarrow English and Tagin → English ranked second, underscoring efficacy of the data augmentation strategies applied.</abstract>
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%0 Conference Proceedings
%T ANVITA : Machine Translation System for Low-resource Indian Languages - Khasi, Nagamese and Tagin
%A J, Siva
%A Kankanwadi, Daneshwari
%A Gugulothu, Abhinav
%A Paul, Biswajit
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F j-etal-2026-anvita
%X 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} \ensuremathłeftrightarrow 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 \ensuremathłeftrightarrow English and Tagin → English ranked second, underscoring efficacy of the data augmentation strategies applied.
%U https://aclanthology.org/2026.wmt-main.155/
%P 2163-2172

Markdown (Informal)

[ANVITA : Machine Translation System for Low-resource Indian Languages - Khasi, Nagamese and Tagin](https://aclanthology.org/2026.wmt-main.155/) (J et al., WMT 2026)

• ANVITA : Machine Translation System for Low-resource Indian Languages - Khasi, Nagamese and Tagin (J et al., WMT 2026)

ACL

• Siva J, Daneshwari Kankanwadi, Abhinav Gugulothu, and Biswajit Paul. 2026. ANVITA : Machine Translation System for Low-resource Indian Languages - Khasi, Nagamese and Tagin. In Proceedings of the Eleventh Conference on Machine Translation, pages 2163–2172, Budapest, Hungary. Association for Computational Linguistics.