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Linguistic coordination is a well-established phenomenon in spokenconversations and often associated with positive social behaviors and outcomes.While there have been many attempts to measure lexical coordination orentrainment in literature, only a few have explored coordination in syntacticor semantic space. In this work, we attempt to combine these different aspectsof coordination into a single measure by leveraging distances in a neural wordrepresentation space. In particular, we adopt the recently proposed WordMover's Distance with word2vec embeddings and extend it to measure thedissimilarity in language used in multiple consecutive speaker turns. Tovalidate our approach, we apply this measure for two case studies in theclinical psychology domain. We find that our proposed measure is correlatedwith the therapist's empathy towards their patient in Motivational Interviewingand with affective behaviors in Couples Therapy. In both case studies, ourproposed metric exhibits higher correlation than previously proposed measures.When applied to the couples with relationship improvement, we also notice asignificant decrease in the proposed measure over the course of therapy,indicating higher linguistic coordination.

Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance