Multi-Modal Agricultural Document Understanding at the DAGRI Task
Zhefan Wang, Da Li, Keping Bi, Nanqing Dong
原文摘要
Agricultural documents, such as prefectural technical reports and crop calendars, are critical knowledge resources. However, their complex table layouts, significant variations in document length, and dense multi-modal elements pose substantial challenges for automated understanding. To address these issues, the NTCIR-19 DAGRI (Data Analytics for aGRicultural Information) task introduced three subtasks focusing on different dimensions of document intelligence. In the Table Information Extraction (TableIE) task, we developed a multi-stage pipeline combining structure-preserving parsing via MinerU, region-adaptive segmentation for long documents, and a dual-modal (vision-text) extraction strategy for complex work schedules. This approach achieved first place with an exact match score of 0.6566. For Multi-Table Multi-Hop Question Answering (MTMH-QA) over interrelated JSON tables, we adopted an agentic search framework utilizing DCI-Agent to iteratively explore directories via bash commands, reinforced by a cross-model validation mechanism. This yielded a revised accuracy of 0.8444, initially ranked first. In the Figure and Table Question Answering (FiT-QA) task, which requires comprehending single large composite images, we implemented a dual-modal VLM pipeline enhanced by deterministic answer normalization, securing the highest BLEU score. Together, our system effectively addresses regional structural variations, dynamic cross-file evidence discovery, and multi-modal alignment, providing a robust framework for complex agricultural document understanding.
cs.IR