Empathetic response from the therapist is key to the success of clinicalpsychotherapy, especially motivational interviewing . Previous work oncomputational modelling of empathy in motivational interviewing has focused on the session-level assessment of therapist empathy . In this position paper, we propose anovel task of turn-level detection of client need for empathy . We propose to leverage pre-trained language models and empathy-related general conversation corpora in a unique labeller-detector framework, where thelabeller annotates a motivational interviewing conversationcorpus with empathy labels to train the detector that determines the need fortherapist empathy .

Author(s) : Zixiu Wu, Rim Helaoui, Vivek Kumar, Diego Reforgiato Recupero, Daniele Riboni

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Code :
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Keywords : empathy - interviewing - motivational - detector - response -

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