  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.

Computation and Language