Towards Detecting Need for Empathetic Response in Motivational Interviewing
This addresses the need for real-time, turn-level empathy detection in clinical psychotherapy to potentially improve therapist training and session outcomes, though it appears incremental as it builds on existing computational empathy modeling.
The paper tackles the problem of detecting when therapists need to provide empathetic responses during motivational interviewing sessions by proposing a turn-level detection task, using a labeller-detector framework with pre-trained language models and empathy-related conversation data to automatically annotate and train the detector.
Empathetic response from the therapist is key to the success of clinical psychotherapy, especially motivational interviewing. Previous work on computational modelling of empathy in motivational interviewing has focused on offline, session-level assessment of therapist empathy, where empathy captures all efforts that the therapist makes to understand the client's perspective and convey that understanding to the client. In this position paper, we propose a novel task of turn-level detection of client need for empathy. Concretely, we propose to leverage pre-trained language models and empathy-related general conversation corpora in a unique labeller-detector framework, where the labeller automatically annotates a motivational interviewing conversation corpus with empathy labels to train the detector that determines the need for therapist empathy. We also lay out our strategies of extending the detector with additional-input and multi-task setups to improve its detection and explainability.