Overview of the CAIL 2023 Argument Mining Track

• URL: http://arxiv.org/abs/2406.14503v1
• Date: Thu, 20 Jun 2024 17:06:13 GMT
• Title: Overview of the CAIL 2023 Argument Mining Track
• Authors: Jingcong Liang, Junlong Wang, Xinyu Zhai, Yungui Zhuang, Yiyang Zheng, Xin Xu, Xiandong Ran, Xiaozheng Dong, Honghui Rong, Yanlun Liu, Hao Chen, Yuhan Wei, Donghai Li, Jiajie Peng, Xuanjing Huang, Chongde Shi, Yansong Feng, Yun Song, Zhongyu Wei,
• Abstract summary: The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. The track consists of two stages, and we introduce the tasks designed for each stage. We outline several submissions that achieve the best results, including their methods for different stages.
• Score: 46.70768209563857
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summarized judgment documents but can also refer to trial recordings. The track consists of two stages, and we introduce the tasks designed for each stage; we also extend the data from previous events into a new dataset -- CAIL2023-ArgMine -- with annotated new cases from various causes of action. We outline several submissions that achieve the best results, including their methods for different stages. While all submissions rely on language models, they have incorporated strategies that may benefit future work in this field.

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