While machine translation has made significant strides for high-resource languages, many regional languages and their dialects, such as the Bangla variants Chittagong and Sylhet, remain underserved. Existing resources are often insufficient for robust sentence-level evaluation and overlook the widespread real-world practice of romanization, the common practice of typing native languages using the Latin script in digital communication. To address these gaps, we introduce BhasaBodh, a comprehensive benchmark for Bangla dialectal machine translation. We construct and release a sentence-level parallel dataset for Chittagong and Sylhet dialects aligned with Standard Bangla and English, create a novel romanized version of the dialectal data to facilitate evaluation in realistic multi-script scenarios, and provide the first comprehensive performance baselines by fine-tuning two powerful multilingual models, NLLB-200 and mBART-50, on seven distinct translation tasks. Our experiments reveal that mBART-50 consistently outperforms NLLB-200 on most dialectal and romanized tasks, achieving a BLEU score as high as 87.44 on the Romanized-to-Standard Bangla normalization task. However, complex cross-lingual and cross-script translation remains a significant challenge. BhasaBodh lays the groundwork for future research in low-resource dialectal NLP, offering a valuable resource for developing more inclusive and practical translation systems.

The advancement of NLP technologies for low-resource and endangered languages is critically hindered by the scarcity of high-quality, parallel corpora. This is particularly true for languages like Chakma, which also faces the challenge of prevalent non-standard, romanized script usage in digital communication. To address this, we introduce ChakmaBridge, the first five-way parallel corpus for Chakma, containing 807 sentences aligned across English, Standard Bangla, Bengali-script Chakma, Romanized Bangla, and Romanized Chakma. Our dataset is created by augmenting the MELD corpus with LLM-generated romanizations that are rigorously validated by native speakers. We establish robust machine translation baselines across six diverse language and script pairs. Our experiments reveal that a multilingual training approach, combining English and Bangla as source languages, yields a dramatic performance increase, achieving a BLEU score of 0.5228 for Chakma translation, a 124% relative improvement over the best bilingual model. We release ChakmaBridge to facilitate research in low-resource MT and aid in the digital preservation of this endangered language.