  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.
