JU - NLP : Improving Low-Resource I ndic Translation System with Efficient L o RA -Based Adaptation (2025)

Abstract: Low-resource Indic languages such as Assamese, Manipuri, Mizo, and Bodo face persistent challenges in NMT due to limited parallel data, diverse scripts, and complex morphology. We address these issues in the WMT $2025$ shared task by introducing a unified multilingual NMT framework that combines rigorous language-specific preprocessing with parameter-efficient adaptation of large-scale models. Our pipeline integrates the NLLB-$200$ and IndicTrans$2$ architectures, fine-tuned using LoRA and DoRA, reducing trainable parameters by over 90% without degrading translation quality. A comprehensive preprocessing suite, including Unicode normalization, semantic filtering, transliteration, and noise reduction, ensures high-quality inputs, while script-aware post-processing mitigates evaluation bias from orthographic mismatches. Experiments across English-Indic directions demonstrate that NLLB-$200$ achieves superior results for Assamese, Manipuri, and Mizo, whereas IndicTrans$2$ excels in English-Bodo. Evaluated using BLEU, chrF, METEOR, ROUGE-L, and TER, our approach yields consistent improvements over baselines, underscoring the effectiveness of combining efficient fine-tuning with linguistically informed preprocessing for low-resource Indic MT.

## introduction low-resource indic languages such as assamese (as), manipuri (mni), mizo (lus), and bodo (brx) pose significant challenges for neural machine translation (nmt) due to data scarcity, script diversity, and linguistic complexity, often leading to suboptimal performance @xcite @xcite . this work aims to address these limitations by developing an efficient, parameter-optimized fine-tuning frame-work tailored for such underrepresented languages in the wmt 2025 shared task @xcite . to address these gaps, we introduce a unified multilingual nmt pipeline tailored for lowresource indic languages, combining robust preprocessing with parameter-efficient fine-tuning methods. we integrate no language left behind (nllb-200) model @xcite and indictrans2 @xcite model, finetuning them using low-rank adaptation (lora) as proposed by @xcite and weight-decomposed low-rank adaptation (dora) as discussed by @xcite to optimize performance while maintaining computational efficiency. our preprocessing pipeline includes unicode normalization, semantic filtering, transliteration @xcite , and noise reduction, ensuring high-quality input data for training. nllb-200, with its extensive multilingual coverage, is adapted for en↔as, mni, and lus, while indictrans2, designed specifically for indic languages, is fine-tuned for en↔brx to leverage its architectural strengths in low-data settings. the methodology ensures fair model comparison by maintaining consistent hyperparameters and evaluation settings across all language pairs, with key contributions lying in the combination of efficient fine-tuning, language-specific preprocessing, and script normalization for indic nmt. our contributions include: (1) the first systematic application of lora/dora to nllb-200 and indictrans2 for low-resource indic languages, reducing trainable parameters by over 90% without sacrificing translation quality; (2) a novel preprocessing framework addressing script diversity and data noise, critical for morphologically complex languages; and (3) a comprehensive evaluation using bleu @xcite , chrf @xcite , meteor @xcite , rouge-l @xcite , and ter @xcite metrics, demonstrating significant improvements over baseline approaches. ## related work early work on translation involving indic languages predominantly used statistical methods and ad-hoc bilingual corpora. for example, @xcite introduced the europarl corpus for smt, but no comparable large-scale corpus existed for low-resource indian languages @xcite . in practice, government and academic groups built phrase-based systems on much smaller data. india's tdil mission developed the sampark and anuvadaksha translation programs by training phrase-based smt models on limited domainspecific corpora. similarly, @xcite compiled sata anuvadak, a set of 110 smt systems across indian language pairs. these efforts established early benchmarks but exposed severe limitations due to data scarcity and domain mismatch. with the advent of neural models, encoder-decoder architectures with attention @xcite and transformers @xcite became standard. researchers trained rnn and then transformer-based nmt systems for english-hindi and other indic pairs, often using byte-pair encoding and shared vocabularies. multilingual and zero-shot strategies @xcite enabled parameter sharing across related languages, benefiting extremely low-resource pairs. shared multilingual models improved translation quality through inductive transfer, as shown in early wmt shared tasks @xcite . indic-to-indic multilingual training further enhanced performance in cases of limited parallel data @xcite . in recent years, large multilingual pre-trained models have been employed for indic mt. models like mbart @xcite and mt5 @xcite provide off-the-shelf improvements, even for indian languages. in parallel, indic-specific models such as indicbart @xcite and indictrans2 @xcite have emerged. these models were trained on carefully normalized indic corpora and have shown superior performance in low-resource translation. indictrans2, in particular, supports translation across all 22 scheduled indian languages and 462 indic language pairs, making it one of the most comprehensive indic mt systems. more recently, ultra-large multilingual models and efficient fine-tuning methods have influenced this domain. the nllb-200 model @xcite introduced a massively multilingual architecture covering 200 languages, with strong performance on low-resource indic pairs. to adapt such models efficiently, lora @xcite and dora @xcite have been proposed, drastically reducing fine-tuning cost while preserving performance. finally, preprocessing methods such as unicode normalization, script unification, and transliteration @xcite have been shown to significantly enhance translation quality for indic languages. these developments form the foundation for recent sota systems tailored to low-resource indic mt. ## analysis of dataset for the machine translation experiments, we utilized the wmt 2025 corpus divided into two categories: category-1 (en ↔ {as, lus, mni}) with moderate training data availability, and category-2 (en ↔ brx) with limited training data. the following sections detail each language pair's parallel corpus specifications. ## results and discussion we evaluate the translation quality of the fine-tuned models using a suite of established automatic evaluation metrics, with results presented in tables 3 and 4 . these results offer key insights into the relative difficulty and success of translating between english and four underrepresented indic languages (i.e., assamese, manipuri, mizo, and bodo) in both directions. ## conclusion this study presents a comprehensive investigation into improving machine translation quality for low-resource indic languages through parameterefficient fine-tuning of large multilingual models. leveraging lora and dora techniques, we finetuned both the nllb-200 and indictrans2 models on a curated and rigorously filtered wmt2025 dataset. our extensive preprocessing pipeline, tailored to address the idiosyncrasies of indic languages, proved essential in ensuring clean and semantically aligned parallel corpora. the empirical results underscore that while nllb-200 exhibits superior performance across most language pairs and metrics, especially in english-to-indic and indic-to-english directions involving assamese, manipuri, and mizo, indictrans2 offers competitive results and even outperforms nllb-200 in the english-to-bodo direction. notably, our integration of script-aware postprocessing and selective transliteration was instrumental in achieving faithful metric evaluations, avoiding script mismatch penalties that would otherwise misrepresent model performance. these findings not only validate the efficacy of lorabased adaptation in low-resource settings but also highlight the value of task-specific linguistic preprocessing for indic languages. our comparative benchmarking, involving multiple metrics, reveals the nuanced translation difficulty across language pairs and emphasizes the importance of directionaware evaluations in multilingual nmt research.