Learning Representation of Therapist Empathy in Counseling Conversation Using Siamese Hierarchical Attention Network

Abstract: Counseling is an activity of conversational speaking between a therapist and a client. Therapist empathy is an essential indicator of counseling quality and assessed subjectively by considering the entire conversation. This paper proposes to encode long counseling conversation using a hierarchical attention network. Conversations with extreme values of empathy rating are used to train a Siamese network based encoder with contrastive loss. Two-level attention mechanisms are applied to learn the importance weights of individual speaker turns and groups of turns in the conversation. Experimental results show that the use of contrastive loss is effective in encouraging the conversation encoder to learn discriminative embeddings that are related to therapist empathy. The distances between conversation embeddings positively correlate with the differences in the respective empathy scores. The learned conversation embeddings can be used to predict the subjective rating of therapist empathy.