AgriGov: New Multilingual Dataset Created for Indian Agricultural Policy Information
Researchers have introduced AgriGov, a curated trilingual dataset covering 50 Indian government agricultural schemes in English, Hindi, and Marathi, comprising approximately 8,000 sentence-aligned parallel pairs. The dataset was built using automated scraping, machine translation pipelines, and human post-editing, then augmented with data from the Samanantar corpus. It aims to enable AI applications—such as question answering and machine translation—that can help farmers access government welfare information in their native languages.
AgriGov is a domain-specific multilingual dataset designed to address the lack of structured, language-grounded resources for Indian agricultural policy and farmer welfare schemes. The dataset covers 50 government schemes sourced from official portals, organized into predefined semantic fields including eligibility criteria, application processes, required documents, and exclusions. Translations from English into Hindi and Marathi were produced through a pipeline combining Google Translate API, MarianMT, and human post-editing, yielding roughly 2,100 source segments. This core dataset was then augmented with sentence pairs from the Samanantar corpus, expanding the total to approximately 8,000 Hindi-Marathi parallel sentence pairs. The project's key methodological contribution is a schema-driven, human-corrected alignment pipeline intended to ensure domain fidelity and reproducibility. AgriGov is designed to support fine-tuning of machine translation models and retrieval-augmented generation systems for farmer-facing tools. The paper has been submitted to the journal Sadhana (Elsevier) and is currently available as a preprint on arXiv.
What's missing
The paper does not appear to report formal quality metrics (e.g., BLEU scores or human evaluation scores) for the translation pipeline, making it difficult to assess the accuracy of the resulting multilingual alignments. It is also unclear whether the dataset has been tested in downstream farmer-facing applications or evaluated by domain experts in agricultural policy. As a preprint, the work has not yet undergone peer review, and the scope is currently limited to three languages, leaving out many other languages spoken by Indian farmers.
What different sources said
- arXiv cs.AICenter
AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
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