A Joint Identification Network for Legal Event Detection

Shutao Gong, Xudong Luo

Abstract: Detecting legal events is a crucial task in legal intelligence that involves identifying event types related to trigger word candidates in legal cases. While many studies have focused on using syntactic and relational dependencies to improve event detection, they often overlook the unique connections between trigger word candidates and their surrounding features. This paper proposes a new event detection model that addresses this issue by incorporating an initial scoring module to capture global information and a feature extraction module to learn local information. Using these two structures, the model can better identify event types of trigger word candidates. Additionally, adversarial training enhances the model’s performance, and a sentence-length mask is used to modify the loss function during training, which helps mitigate the impact of missing trigger words. Our model has shown significant improvements over state-of-the-art baselines, and it won third prize in the event detection task at the Challenge of AI in Law 2022 (CAIL 2022).

External IDs:dblp:conf/iconip/GongL23