MTL-DQA: Multi-Task Learning with Psychological Indicators for Enhanced Quality in Depression Community QA System

Depression is a prominent public mental health issue affecting millions globally. With the growth of online communities, many individuals suffering from depression are increasingly seeking guidance and support from community-based Question-Answering (QA) systems. Previous methods, which recommend the best answer by calculating the semantic distance between a question and its potential answers, seldom incorporate psychological indicators as features. Answers from non-professionals might offer misguided information to those with depression. In our approach, we employ a multi-task learning method, taking into account both semantic information and psychological indicators for answer selection in depression community QA systems. To compute the semantic similarity between a given question and its potential answers, we gather surface-layer textual information. We then evaluate these candidate answers using psychological indicators from two dimensions: (1) employing five expert-defined comprehensive quality indicators to assess answer quality, and (2) using existing standard answers, provided by experts for various question categories, as external references to automatically review the candidate answers. Finally, by integrating the semantic similarity and psychological indicators, we make the final answer selection. Experimental results have shown that our model significantly enhances answer recommendations in the mental health domain.