ANVITA : A Multi-pronged Approach for Enhancing Machine Translation of Extremely Low-Resource I ndian Languages (2025)

Abstract: 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.

## introduction india is home to 22 official languages, 30 languages with a million plus native speakers and 122 languages with more than 10,000 speakers 1 . most of these 122 languages fall into low and extremely low resource categories, where availability of parallel corpora is very limited. moreover, 1 https://censusindia.gov.in/census.website/data/censustables many of these languages do not only suffer from scarcity of parallel corpora but also from quality monolingual corpora and lack of language processing resources and tools, making the development of nlp solutions, including machine translation, particularly challenging. wmt 2025 shared task on low-resource indic language translation foot_0 presented a novel challenge of building robust machine translation system for low-resource indic languages from diverse language families. the task focused on translation of seven north-eastern languages to and from english and comprises of (i) assamese (an indo-aryan language spoken mainly in the northeastern indian state of assam), (ii) mizo (a sino-tibetan language spoken primarily in the mizoram state of india), (iii) khasi (an austroasiatic language spoken in meghalaya, india), (iv) manipuri/meiteilon (a sino-tibetan language and the official language of manipur, india ), (v) nyishi (a sino-tibetan language of arunachal pradesh, india), (vi) bodo (a sino-tibetan language of assam) and (vii) kokborok language (a sino-tibetan language spoken primarily by the tripuri people). with the dawn of neural machine translation (nmt) @xcite and availability of parallel corpora, translation systems have achieved significant performance for high and medium resource languages. however quality machine translation system for the low-resource or extremely low-resource languages still remains a major challenge as nmt architectures (supervised) based on encoder-decoder framework need large parallel corpora; the more quality parallel data is available, the better nmt system can learn accurate and fluent translations. a large number of indian languages are individually resource poor. development of machine translation systems for low resource indian languages poses sig-nificant challenges due to the scarcity of parallel corpora and limited linguistic resources including tools. open source indictrans2 @xcite is a pretrained multilingual machine translation model, specifically designed for the translation of 22 officially recognized indian languages and does not support many low resource indian languages such as khasi, nyishi and kokborok. these low-resource indian languages remained under-represented in other popular pretrained language models such as byt5 @xcite and indic bert @xcite and similarly in popular pretrained machine translation models such as @xcite . however some of the recent development in techniques such as sub-word tokenization @xcite , use of monolingual data, data-augmentation techniques such as back translation @xcite , transfer learning, peft @xcite over suitably selected multilingual plm (pre-trained language models) @xcite and pre-trained multilingual translation models @xcite did open up gates for improving translation systems for low-resource language pairs. this paper presents anvita indic lr machine translation system, submitted to wmt 2025 shared task on low-resource indic language by anvita team. our team focused on three languages khasi (kha), nyishi(njz), and kokborok (trp) and participated in six translation directions translating both to and from english. anvita's strategy involved several key steps: as part of synthetic parallel corpora generation back translation is carried out using openly available third party translation tool. anvita also employed pre-processing with set of language agnostic heuristics and selective post edits using llm. on the wmt 2025 official test set, an-vita achieved bleu score of 2.41-11.59 with 2.2k to 60k corpora and 6.99-19.43 bleu scores with the augmented corpora. overall anvita ranked first for {nyishi, kokborok} ↔ english and second for khasi ↔ english on the official test set across all the evaluation metrics used by the organizer. the rest of the paper is organized as given below. wmt 2025 task set up is described in 2. section 3 presents related work. brief introduction to datasets is given in section 4. anvita indic lr system is described in section 5. section 5.5 describes the experimental setup. section 6 reports evaluation results. section 7 concludes the paper along with future directions. anvita team submissions included three languages ( khasi, nyishi, kokborok ), six translation directions ( khasi↔english, nyishi↔english, kokborok↔english ) and 17 systems comprising six primary and eleven contrastive systems. ## related work building quality machine translation systems for low resource languages remains a critical research area. nllb (no language left behind) @xcite is one such research initiative to address limitations of mt for low resource languages which built large-scale multilingual model for 200 languages (including few lowresource indian languages) on curated multilingual corpora, using transformer-based architecture @xcite . nllb showed strong zeroshot and few-shot translation capabilities. as, fine-tuning such large models remains computationally expensive, our approach involved use of low-rank adaptation (lora) @xcite , a method for parameter-efficient fine-tuning of large models to significantly reduce the number of update parameters. nllb uses sub-word tokenizers such as sentencepiece @xcite or bpe. to have a token free approach towards wider inclusion, byt5 @xcite introduced a byte-level variant of the t5 model @xcite , which operates at the byte level rather than the token or sub-word level. our work builds on these foundations. we leverage the methodologies and insights gained from wmt 2024's indic mt shared task @xcite efforts but extend them to the more challenging low resource languages like kokborok and khasi. ## dataset the training dataset provided by the wmt 2025 organizer varies from 60,000 to as low as 2,269 sentences as summarized in table-1. anvita primary submissions used only parallel corpora provided by the wmt 2025 organizer. contrastive systems however used additional parallel data curated as part of this work and is summarized in table-2. for evaluation, the official test set used is summarized in table-3 ## anvita indic lr machine translation system this section describes the design of anvita systems for three low resource languages nyishi, khasi and kokborok. ## conclusion wmt 2025 shared task on low-resource indic language translation posed significant challenging problem of building robust mt system for extremely low-resource indian languages, many of which have little presence in digital domain. an-vita team utilizing multi-pronged approach with transfer learning strategy achieved bleu score of 2.41-11.59 for 2.2k to 60k corpora and secured top ranks. overall with augmented data, the team achieved bleu score of 6.99-19.43 which also 6 https://huggingface.co/ fell short of robust mt system threshold indicating need for data augmentation in terms of both quality corpora and synthetic data and innovative techniques for data generation, suitable architectural enhancement and better learning objectives.