Overview of the CAIL 2023 Argument Mining Track
This work addresses the problem of argument mining in legal texts for researchers and practitioners in AI and law, but it is incremental as it builds on previous events and datasets.
The paper overviews the CAIL 2023 Argument Mining Track, which aims to identify and extract interacting argument pairs in trial dialogs, resulting in a new dataset CAIL2023-ArgMine and highlighting top-performing submissions that use language models with novel strategies.
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.