VLSP 2025 MLQA-TSR Challenge: Vietnamese Multimodal Legal Question Answering on Traffic Sign Regulation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Luu, Son T. Vo, Trung Nguyen, Hiep Tran, Khanh Quoc Van Nguyen, Kiet Tran, Vu Nguyen, Ngan Luu-Thuy Nguyen, Le-Minh |
| author_facet | Luu, Son T. Vo, Trung Nguyen, Hiep Tran, Khanh Quoc Van Nguyen, Kiet Tran, Vu Nguyen, Ngan Luu-Thuy Nguyen, Le-Minh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VLSP 2025 MLQA-TSR Challenge: Vietnamese Multimodal Legal Question Answering on Traffic Sign Regulation Luu, Son T. Vo, Trung Nguyen, Hiep Tran, Khanh Quoc Van Nguyen, Kiet Tran, Vu Nguyen, Ngan Luu-Thuy Nguyen, Le-Minh Computation and Language Artificial Intelligence 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. |
| title | VLSP 2025 MLQA-TSR Challenge: Vietnamese Multimodal Legal Question Answering on Traffic Sign Regulation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.20381 |