构建多语言农业政策数据集，助力农民获取政府补贴信息。
AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
- 基于50项政策自动采集并结构化数据，覆盖资质、流程等关键字段。
- 生成约2100个英-印-马三语段落，经人工校对确保准确性。
- 适合做农业政策翻译、问答系统或农民服务工具的开发者使用。
AgriGov 是一个经过精心整理的三语（英语-印地语-马拉地语）数据集，旨在解决农业政策与农民福利计划领域缺乏领域适配的多语言资源问题。我们最初通过自动化爬取技术，从可信政府门户收集并结构化了50项政策，将其归入预定义的语义字段（如标题、资格条件、申请流程、所需文件、排除条款）。使用 Google Translate API、MarianMT 及人工后编辑相结合的流水线完成翻译，生成约2100个源段落的印地语-马拉地语数据集。为进一步提升覆盖范围，我们引入了Samanantar语料库中的句子，最终获得约8000组句对齐的印地语-马拉地语平行语料。该数据集可有效用于该领域的机器翻译模型微调。AgriGov 的核心贡献在于一套基于模式的、人工校正的多语言对齐流程，确保领域一致性、提供来源追溯，并支持可复现实验，为面向农民的检索增强型应用提供可靠基础。
原文摘要 · Abstract (English)
AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes. Initially, we collected and structured data from 50 government schemes sourced from trusted portals using automated scraping techniques, organizing it into predefined semantic fields (e.g., title, eligibility, application process, documents, exclusions). Translations were performed using a pipeline combining Google Translate API, MarianMT, and human post-editing, resulting in a domain-specific Hindi-Marathi dataset comprising approximately 2100 source segments. To enhance coverage, we augmented this dataset with sentences from the Samanantar corpus, leading to approximately 8,000 sentence-aligned Hindi-Marathi parallel pairs. The dataset now offers robust resources for fine-tuning machine translation models in this domain. AgriGov is designed for applications in domain-adaptive machine translation, question answering, information retrieval, and summarization systems. Its key contribution is a schema-driven, human-corrected multilingual alignment pipeline that ensures domain fidelity, provides provenance, and supports reproducible experiments, enabling retrieval-augmented applications for farmer-facing tools.
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