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Counseling is an activity of conversational speaking between a therapist anda client. Therapist empathy is an essential indicator of counseling quality andassessed subjectively by considering the entire conversation. This paperproposes to encode long counseling conversation using a hierarchical attentionnetwork. Conversations with extreme values of empathy rating are used to traina Siamese network based encoder with contrastive loss. Two-level attentionmechanisms are applied to learn the importance weights of individual speakerturns and groups of turns in the conversation. Experimental results show thatthe use of contrastive loss is effective in encouraging the conversationencoder to learn discriminative embeddings that are related to therapistempathy. The distances between conversation embeddings positively correlatewith the differences in the respective empathy scores. The learned conversationembeddings can be used to predict the subjective rating of therapist empathy.

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