Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT : A Case Study On B hili- H indi- E nglish Parallel Corpus (2025)

Abstract: The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili, which lack high-quality linguistic resources. This paper addresses the gap by introducing Bhili-Hindi-English Parallel Corpus (BHEPC), the first and largest parallel corpus worldwide comprising 110,000 meticulously curated sentences across Bhili, Hindi, and English. The corpus was created with the assistance of expert human translators. BHEPC spans critical domains such as education, administration, and news, establishing a valuable benchmark for research in low resource machine translation. To establish a comprehensive Bhili Machine Translation benchmark, we evaluated a wide range of proprietary and open-source Multilingual Large Language Models (MLLMs) on bidirectional translation tasks between English/Hindi and Bhili. Comprehensive evaluation demonstrates that the fine-tuned NLLB-200 distilled 600M variant model outperforms others, highlighting the potential of multilingual models in low resource scenarios. Furthermore, we investigated the generative translation capabilities of multilingual LLMs on BHEPC using in-context learning, assessing performance under cross-domain generalization and quantifying distributional divergence. This work bridges a critical resource gap and promotes inclusive natural language processing technologies for low-resource and marginalized languages globally.

## introduction india is a linguistically diverse country, with over 1, 369 distinct mother tongues reported in the 2011 census and 22 constitutionally recognized languages under the 8 th schedule. however, despite this linguistic richness, many indigenous and tribal languages remain critically endangered due to an acute scarcity of digitized linguistic resources and parallel corpora. indigenous languages are deeply intertwined with cultural heritage, robust translation systems are imperative not only for effective communication and social inclusion but also for equitable access to government services @xcite . effective translation supports crucial national activities ranging from the dissemination of policy directives and welfare schemes to judicial processes and educational initiatives, thereby reinforcing national cohesion within india's intricate multilingual landscape @xcite . efforts to improve machine translation (mt) for indic languages have accelerated, with early initiatives like the hindi-english mt challenge at wmt'14 @xcite facilitating subsequent benchmarks for gujarati-english @xcite and tamil-english @xcite . recent large-scale efforts such as workshop on asian translation(wat) @xcite , flores 101 @xcite nllb @xcite and indicgen-bench @xcite have expanded linguistic coverage significantly by incorporating multiple indic languages into mt benchmarks. parallel advances in large language models (llms) @xcite @xcite have revolutionized text generation and translation tasks. howerver, contemporary multilingual models, predominantly trained on high-resource languages, inherently carry cultural biases, leading to suboptimal performance on underrepresented languages. in this context, the bhili language, spoken by approximately 13 million people, foot_0 and written in the devanagari script, is notably underserved. the lack of parallel corpora has severely impeded the development of effective mt systems for bhili. to address this gap, we present the bhili-hindi-english parallel corpus (bhepc), a meticulously curated, community-driven, high-quality corpus comprising 110,000 aligned sentences in bhili, hindi, and english. leveraging richer linguistic resources available in hindi and english, bhepc enables the exploration of transfer learning and cross-lingual methodologies in an extremely lowresource setting. although this study focuses on bhili, the proposed corpus construction workflow, starting from a seed set and iteratively expanded through model-assisted generation with native speaker post-editing, offers a scalable methodology that can be adapted to other endangered languages with minimal digital presence. building on this foundation, our primary contributions are: ## background large language models heavily rely on large annotated corpora, unintentionally favoring highresource languages, leaving low-resource languages, particularly several regional languages of india, notably underrepresented in computational models and digital repositories. this discrepancy necessitates targeted initiatives for creating community-driven, gold-standard datasets to capture cultural nuances and ensure digital equity and cultural preservation. the introduction of transformer-based models @xcite , has brought about a paradigm change in neural machine translation (nmt). despite these advances, translating extremely low-resource regional indic languages such as bhili, remains challenging due to inadequate parallel and monolingual data. while transfer learning @xcite and multilingual nmt @xcite partly mitigate data scarcity, their effectiveness is still limited by reliance on parallel corpora. concurrent research has also focused on building natural language understanding models @xcite and comprehensive evaluation datasets @xcite for indic languages, thus facilitating systematic benchmarking and comparison. recent multilingual models like indictrans2 (gala and chitale, 2023) and muril @xcite offer improved translation and natural language understanding capabilities, leveraging synthetic data and transliteration to compensate for resource constraints. meanwhile, large language models @xcite @xcite demonstrate significant potential for crosslingual transfer, yet their applicability to translation tasks without direct parallel supervision remains underexplored. collectively, these advancements highlight the necessity of developing robust community-driven gold standard datasets, evaluation benchmarks, and specialized architectures to enhance translation for severely underrepresented languages like bhili. ## dataset the acute scarcity of publicly available parallel corpora continues to hinder the development of neural mt models for languages such as bhili. given bhili's linguistic and cultural significance, we address this gap by curating a large-scale, goldstandard parallel corpus translated by native speakers through community-driven efforts, constructing robust evaluation benchmarks, and leveraging multilingual models to exploit linguistic similarities across indic languages. ## human evaluation and qualitative analysis evaluating mt models for low-resource languages like bhili necessitates a comprehensive assessment framework that combines both quantitative human judgments and qualitative error analysis. this dual perspective ensures a deeper understanding of translation quality beyond surface-level automatic metrics, addressing the linguistic and cultural complexities inherent in low-resource settings. ## conclusion in this work, we present the first large-scale bhili-hindi-english parallel corpus (bhepc) and establish strong benchmarks for bhili translation. our data acquisition methodology was both resourceintensive and community-engaged, reflecting the complexities of collecting high-quality linguistic data for an under-documented language. to assess whether llms trained on high-resource devanagari-script languages generalize to bhili, we conducted extensive benchmarking. while bhili shares the devanagari script with hindi, our findings revealed significant performance degradation particularly in generation tasks challenging the assumption of positive transfer. this observation underscores a pivotal insight script-level similarity alone is insufficient to guarantee semantic transfer or effective representation learning. bhili's distinct linguistic and cultural characteristics remain underrepresented in existing models, underscoring the need for dedicated llms tailored to low-resource languages like bhili.