mlsanityData to 14 Oct 2026

AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers

arXiv 2606.08272 ↗First posted 6 Jun 2026cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 6 Jun 2026.

Builds on

11 of 13 references.

References · 11

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  3. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  4. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  6. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  7. 2018Marian: Fast Neural Machine Translation in C++cited by 203
  8. 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
  9. 2023IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languagescited by 152
  10. 2021Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languagescited by 136
  11. 2024Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)cited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025AgriGPT: a Large Language Model Ecosystem for Agriculture
  2. 2025Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
  3. 2025AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence
  4. 2024AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
  5. 2025AgriGPT-VL: Agricultural Vision-Language Understanding Suite
  6. 2026Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory
  7. 2026AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding
  8. 2024AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning