Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance
This work addresses the need for a comprehensive measure of linguistic coordination in conversations, particularly for applications in clinical psychology, but it is incremental as it builds on existing methods like Word Mover's Distance.
The authors tackled the problem of measuring linguistic coordination in conversations by combining lexical, syntactic, and semantic aspects into a single metric using Word Mover's Distance with word2vec embeddings, and found it correlated better with therapist empathy and affective behaviors in clinical psychology case studies than previous measures, showing higher correlation and a significant decrease in coordination over therapy for improved couples.
Linguistic coordination is a well-established phenomenon in spoken conversations and often associated with positive social behaviors and outcomes. While there have been many attempts to measure lexical coordination or entrainment in literature, only a few have explored coordination in syntactic or semantic space. In this work, we attempt to combine these different aspects of coordination into a single measure by leveraging distances in a neural word representation space. In particular, we adopt the recently proposed Word Mover's Distance with word2vec embeddings and extend it to measure the dissimilarity in language used in multiple consecutive speaker turns. To validate our approach, we apply this measure for two case studies in the clinical psychology domain. We find that our proposed measure is correlated with the therapist's empathy towards their patient in Motivational Interviewing and with affective behaviors in Couples Therapy. In both case studies, our proposed metric exhibits higher correlation than previously proposed measures. When applied to the couples with relationship improvement, we also notice a significant decrease in the proposed measure over the course of therapy, indicating higher linguistic coordination.