End-to-end interactive joint model: Clause-phrase multi-task learning for suicidal ideation cause extraction (SICE) in Chinese Weibo text

Abstract

Suicide prevention has been a critical research focus for governments, mental health professionals, and social work researchers worldwide. With the increasing number of individuals seeking help through social networks and psychological counseling platforms, timely analysis of the causes of Suicidal Ideation (SI) in help-seeking texts can provide scientific evidence and actionable insights for suicide prevention efforts. Existing approaches face challenges: (i) SIC clause extraction is coarse-grained and thus imprecise in localization; (ii) SIC phrase extraction is more precise but inherently harder. To address this, we propose an end-to-end interactive joint model (EIJM) based on a clause-phrase multi-task learning (MTL) framework, where SIC phrase extraction serves as the main task and SIC clause extraction as the auxiliary task. By leveraging joint learning, EIJM enhances extraction accuracy while reducing task difficulty. Experimental results demonstrate that EIJM outperforms the two-stage independent multi-task (2SIM) approach across multiple evaluation metrics. Specifically, in the SIC phrase extraction task, EIJM achieves a 1.1 % improvement in recall over 2SIM without compromising precision. In the SIC clause extraction task, EIJM improves precision, recall, and F1-score by 0.4 %, 0.9 %, and 0.7 %, respectively. Furthermore, in 2SIM, incorporating clause-level representations from the auxiliary task into the main task enhances local matching and fuzzy matching metrics, with the Fuzzy Match method improving the most by 0.9 %. However, it yielded limited improvement in exact matching performance.