arXiv:2605.27050v1 Announce Type: cross Abstract: We present BhashaSetu, a linguistically enriched English--Marathi parallel dataset addressing persistent data limitations in low-resource neural machine translation (NMT). Marathi, spoken by over 95 million people, remains underrepresented in high-quality parallel corpora across diverse domains. Our dataset comprises 2.78 million sentence pairs from heterogeneous sources including news, politics, healthcare, literature, and culture, with stemmed and lemmatized representations to support morphology-aware analysis. We benchmark multiple state-of-
The increasing focus on AI democratization and the recognition of linguistic diversity for broader AI adoption necessitate the development of robust datasets for low-resource languages.
This development allows for more inclusive and globally relevant AI systems, reducing the dominance of major languages in AI training and application, facilitating indigenous AI development.
The availability of high-quality parallel datasets like BhashaSetu enables more accurate machine translation for low-resource languages, fostering digital inclusion and potentially fueling local AI innovation.
- · India
- · Marathi speakers
- · Low-resource language AI developers
- · Multilingual tech platforms
- · Providers of English-centric NMT for India
- · Translation services without NMT integration
Improved machine translation capabilities for Marathi and other similar low-resource languages will emerge.
This could lead to a proliferation of AI applications and services tailored for local linguistic contexts, fostering economic growth and digital literacy in underserved communities.
The success of such data-centric approaches might inspire other nations to invest in similar initiatives for their indigenous languages, potentially accelerating a global trend towards localized AI stacks.
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