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Response Selection for a Virtual Counsellor

Chatbots are increasingly used for delivering mental health assistance. As part of our effort to develop a chatbot on academic and social issues for Cantonese-speaking students, we have constructed a dataset of 1,028 post-reply pairs on test anxiety and loneliness. The posts, harvested from Cantonese social media, are manually classified to a symptom category drawn from counselling literature; the replies are human-crafted, offering brief advice for each post. For response selection, the chatbot predicts the quality of a candidate post-reply pair with a regression model. During training, the symptom categories were used as proxies of reply relevance. In experiments, this approach improved response selection accuracy over a binary classification model and a weakly supervised regression model. This result suggests that manual annotation of symptom category can help boost the performance of a counsellor chatbot.