Instruction-Tuned E nglish to B hojpuri Neural Machine Translation Using Contrastive Preference Optimization (2025)

Abstract: This paper presents an English to Bhojpuri machine translation (MT) system developed for the WMT25 General MT Shared Task. Given the low-resource nature of Bhojpuri, we adopt a two-stage training pipeline: unsupervised pretraining followed by supervised fine-tuning. During pretraining, we use a 300,000-sentence corpus comprising 70% Bhojpuri monolingual data and 30% English data to establish language grounding. The fine-tuning stage utilizes 29,749 bilingual English to Bhojpuri sentence pairs (including training, validation, and test sets). To adapt the system to instruction-following scenarios, we apply a novel optimization strategy: Contrastive Preference Optimization (CPO). This technique enables the model to capture fine-grained translation preferences and maintain semantic fidelity in instruction-tuned settings. We evaluate our system across multiple metrics, demonstrating moderate performance in low-resource MT tasks, particularly in diverse domains such as literary, news, social, and speech.

## introduction machine translation (mt) plays a pivotal role in promoting digital inclusion and language equity in today's interconnected world. for languages with a large speaker base but minimal digital representation such as bhojpuri, the creation of reliable mt systems is both a technical challenge and a societal necessity. bhojpuri foot_0 , an indo-aryan language spoken by more than 50 million people in india and nepal, remains severely underrepresented in natural language processing (nlp) research. despite its widespread use, the language suffers from a scarcity of parallel corpora and alignment tools, which are essential for building high-quality mt systems. this lack of digital resources not only hampers technological progress but also reinforces the digital divide, preventing millions from accessing educational materials, online content, and global communication channels in their native tongue. addressing these gaps is crucial, as advancements in low-resource @xcite directly contribute to equitable access to information, cultural preservation, and greater participation in the global digital economy. this paper presents our english to bhojpuri mt system submitted to wmt25, developed with a focus on overcoming the challenges posed by limited linguistic resources @xcite . our approach integrates two core strategies: these methods are designed to optimize performance under data-scarce conditions while maintaining linguistic and cultural fidelity. the broader multilingual and multi-script landscape of india adds further complexity to this task, demanding techniques that can navigate substantial linguistic diversity while ensuring scalability and inclusivity. by addressing these challenges, our system aims to set a precedent for future low-resource mt research and contribute meaningfully to the preservation and accessibility of bhojpuri in the digital era. the principal contributions of this work are as follows: ## dataset our work leverages a combination of monolingual corpora, bilingual parallel data, and instruction-response pairs to develop an @xmath0. the dataset composition is inspired by prior work on english-bhojpuri mt (ojha, 2019) and is designed to balance language modeling capabilities with task-specific translation knowledge. ## evaluation results we evaluated our english-bhojpuri mt system using both traditional surface-form metrics and modern semantic and llm-based evaluation frameworks. the chrf++ @xcite metric evaluates character-and word-level n-gram overlap with reference translations, providing a surfacelevel similarity perspective. learned metrics such as cometkiwi-xl @xcite and xcomet-xl model semantic alignment directly using multilingual embeddings, making them more sensitive to meaning preservation in low-resource (?)contexts. llm-based evaluation metrics, such as gemba-esa-cmda and gemba-esa-gpt4.1 @xcite , leverage large language models to judge translation quality in a more human-like manner. finally, metricx-24-hybrid-xl integrates multiple embedding spaces @xcite but can be sensitive to domain mismatch in morphologically rich, lowresource settings, as reflected by its negative score. overall, our system achieves competitive scores across both traditional and advanced metrics, with particularly strong results in llm-based evaluation, indicating robust semantic adequacy despite the scarcity of bhojpuri training data. ## conclusion this paper introduces a robust english to bhojpuri machine translation system that leverages a twostage training pipeline: unsupervised pretraining on extensive monolingual data and supervised finetuning on high-quality bilingual corpora. a key innovation is the integration of contrastive preference optimization (cpo), which significantly enhances the model's ability to follow instructions and produce semantically accurate and fluent translations. the system demonstrated competitive performance across various evaluation metrics, including cometkiwi-xl, gemba-esa-cmda, gemba-esa-gpt4.1, metricx-24-hybrid-xl, xcomet-xl, and chrf++. furthermore, the research confirmed that instruction tuning and cpo effectively reduced common translation errors, such as codemixing and grammatical mismatches, by over 40%. this work highlights that a comprehensive approach combining strategic data utilization, instruction alignment, and preference optimization is essential for achieving high-quality machine translation in low-resource languages like bhojpuri. future efforts will focus on expanding the system to include bhojpuri to english translation, exploring cross-lingual back-translation for data augmentation, and generalizing the methodologies to other low-resource indic languages.