构建越南交通标志法规多模态问答基准，推动法律文本智能处理
VLSP 2025 MLQA-TSR Challenge: Vietnamese Multimodal Legal Question Answering on Traffic Sign Regulation
- 设计双任务评测体系：多模态法律检索与问答
- 问答任务准确率达86.30%，检索任务F2得分为64.55%
- 聚焦越南交通法规，为本地化法律AI提供数据基础
本文介绍了VLSP 2025 MLQA-TSR——VLSP 2025上关于越南交通标志法规的多模态法律问答共享任务。该任务包含两个子任务：多模态法律检索与多模态问答，旨在推进越南多模态法律文本处理研究，并为构建和评估多模态法律领域智能系统提供基准数据集，重点关注越南交通标志法规。在该任务中，最佳报告结果为多模态法律检索的F2得分为64.55%，多模态问答的准确率为86.30%。
原文摘要 · Abstract (English)
This paper presents the VLSP 2025 MLQA-TSR - the multimodal legal question answering on traffic sign regulation shared task at VLSP 2025. VLSP 2025 MLQA-TSR comprises two subtasks: multimodal legal retrieval and multimodal question answering. The goal is to advance research on Vietnamese multimodal legal text processing and to provide a benchmark dataset for building and evaluating intelligent systems in multimodal legal domains, with a focus on traffic sign regulation in Vietnam. The best-reported results on VLSP 2025 MLQA-TSR are an F2 score of 64.55% for multimodal legal retrieval and an accuracy of 86.30% for multimodal question answering.
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