Mental Health Text Matching Model Integrating Characters’ Mental Portrait

The global situation regarding mental health problems is a matter of serious concern. Unfortunately, in many countries, the shortage of mental health practitioners often leaves individuals with mental health problems without access to professional services. The retrieval-based community question answering for mental health can provide self-service information for those who are suffering from mental health problems. There are two different matching criteria to consider when matching question post and support response in this task, namely semantic matching and mental health matching. To address the issue of excessive non-ideal support responses in a large pool of candidate support responses, we propose a two-stage text matching model that integrates characters’ mental portraits using a multi-layer attention mechanism. This method divides our task into two sequential subtasks: a retrieval task and a matching task, respectively. In the first stage, a retrieval sub-method is used to retrieve a set of candidate support responses with a certain semantic similarity for each question post. In the second stage, the matching sub-method performs matching judgments based on the results generated in the previous stage. This sub-method applies a multi-layer attention mechanism to effectively integrate characters’ mental portraits and semantics. This process can modify the results of simple semantic matching. Experimental results show that T2CMP proposed in this paper is superior to state-of-the-art methods.