Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling

• URL: http://arxiv.org/abs/2301.12729v1
• Date: Mon, 30 Jan 2023 08:53:35 GMT
• Title: Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling
• Authors: Aseem Srivastava, Ishan Pandey, Md. Shad Akhtar, Tanmoy Chakraborty
• Abstract summary: We present READER, a dialogue-act guided response generator for mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1) We evaluate READER on HOPE, a benchmark counseling conversation dataset.
• Score: 25.524804770124145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain -- the prime reason is the lack of understanding in mental health counseling. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics. To this end, we propose READER -- a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by d(t+1) and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics -- METEOR, ROUGE, and BERTScore. We also furnish extensive qualitative and quantitative analyses on results, including error analysis, human evaluation, etc.

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