D o DS - IITPKD :Submissions to the WMT 25 Low-Resource I ndic Language Translation Task (2025)

Abstract: Low-resource translation for Indic languages poses significant challenges due to limited parallel corpora and linguistic diversity. In this work, we describe our participation in the WMT 2025 shared task for four Indic languages-Khasi, Mizo, Assamese, which is categorized into Category 1 and Bodo in Cate- gory 2. For our PRIMARY submission, we fine- tuned the distilled NLLB-200 model on bidi- rectional English↔Khasi and English↔Mizo data, and employed the IndicTrans2 model family for Assamese and Bodo translation. Our CONTRASTIVE submission augments training with external corpora from PMIN- DIA and Google SMOL to further enrich low- resource data coverage. Both systems lever- age Low-Rank Adaptation (LoRA) within a parameter-efficient fine-tuning framework, en- abling lightweight adapter training atop frozen pretrained weights. The translation pipeline was developed using the Hugging Face Trans- formers and PEFT libraries, augmented with bespoke preprocessing modules that append both language and domain identifiers to each instance. We evaluated our approach on par- allel corpora spanning multiple domains- ar- ticle based, newswire, scientific, and biblical texts as provided by the WMT25 dataset, under conditions of severe data scarcity. Fine-tuning lightweight LoRA adapters on targeted parallel corpora yields marked improvements in evalua- tion metrics, confirming their effectiveness for cross-domain adaptation in low-resource Indic languages.

## introduction low-resource language translation remains one of the most persistent challenges in machine translation (mt), particularly for linguistically diverse regions such as india. we observed that in wmt25 the provided corpora spanned biblical, scientific, * work done at iit palakkad. ## related work translation quality in low-resource scenarios has been significantly advanced by large-scale multilingual models and lexical augmentation techniques. @xcite introduced no language left behind (nllb) which demonstrates effective multilingual mt at scale using a sparsely gated mixture of expert models trained with data that is mined specifically for underrepresented languages. their approach achieved substantial bleu improvements and incorporated safety evaluations using flores-200 @xcite . also, @xcite explored bilingual lexica as a lightweight data augmentation method, showing that collected lexical resources such as gatitos can significantly enhance performance in unsupervised translation settings. toolkits like the huggingface datasets library @xcite also made efforts to support data development and reproducibility, which standardizes access to hundreds of multilingual corpora used in mt research. for evaluation, several automatic metrics have been proposed to correlate better with human judgments. @xcite developed rouge, widely used in summarization but also adopted in mt, which computes n-gram overlap and has influenced newer evaluation benchmarks. @xcite introduced meteor, which matches unigrams using surface forms, stems and synonyms, incorporating both precision and recall as well as word order. @xcite proposed translation edit rate (ter), also called translation error rate, which measures the number of edits required to change a system output into one of the references. @xcite proposed chrf, a character n-gram f-score metric that outperforms word-level metrics in many segment-level evaluations. ## dataset for our primary submission, we utilized the indic machine translation corpus from the wmt25 shared task. this benchmark comprises parallel data for four low-resource indian languages, stratified into two categories based on training data volume. category 1 encompasses language pairs with moderate-sized corpora, whereas category 2 contains the severely data-starved corpora. the language pairs are delineated as follows: category 1: en-as (english ↔ assamese), enlus (english ↔ mizo), en-kha (english ↔ khasi) category 2: en-bodo (english ↔ bodo) the parallel corpora supplied by the wmt25 indicmt shared task 1 were employed for all model development. each language pair's dataset was randomly divided into training (70 %), validation (20 %), and internal test (10 %) subsets, as detailed in ## methodology our methodology is focused on fine-tuning stateof-the-art, pre-trained multilingual translation models that excel in low-resource settings. we chose nllb-200(600m) @xcite and indic-trans2 @xcite as our core architectures. nllb-200(600m) , developed under the no language left behind initiative, delivers extensive typological coverage and consistently high translation quality across diverse languages @xcite . indictrans2, by contrast, incorporates script-aware tokenization and subword segmentation tailored specifically to indian languages, yielding superior performance on indic↔english pairs @xcite . by fine-tuning these complementary models on the wmt25 indicmt parallel corpora and on the augmented corpus for our contrastive system, we established a strong performance baseline and systematically quantified the gains afforded by data augmentation. ## results we evaluate our system submissions on the wmt indicmt shared task for four low-resource indian languages: assamese, khasi, mizo, and bodo. tables 3 presents the comprehensive results for our primary and contrastive submissions respectively across all bidirectional translation pairs. all systems are evaluated using standard automatic metrics including bleu @xcite , me-teor @xcite , rouge-l @xcite , chrf @xcite , and ter @xcite . the results demonstrate that our contrastive submissions generally achieved better or comparable performance across most language pairs and metrics compared to the primary submissions. ## conclusion in this paper, we described the dods-iitpkd submissions to the wmt25 low-resource indic language translation task. our systems were designed for multiple indic-english and english-indic translation directions, focusing particularly on category-i languages of northeast india. we explored a combination of pre-trained multilingual models (indictrans, nllb-200(600m)),finetuning strategies and lora-based efficient adaptation. future work will focus on more domainrobust adaptation and incorporating quality estimation for improved translation reliability.