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

arXiv: https://arxiv.org/abs/1904.06002 · HTML: https://mlsanity.com/paper/1904.06002/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations](https://mlsanity.com/paper/2102.11573/) (2021)
- [Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies](https://mlsanity.com/paper/2102.11265/) (2021)
- [Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy](https://mlsanity.com/paper/2410.07147/) (2024)
- [A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations](https://mlsanity.com/paper/2109.01924/) (2021)
- [WMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover's Distance in Sociocultural Analysis](https://mlsanity.com/paper/2110.07330/) (2021)
- [Lexical Entrainment for Conversational Systems](https://mlsanity.com/paper/2310.09651/) (2023)
- [Information Dynamics of Language Communication](https://mlsanity.com/paper/2606.30096/) (2026)

## Abstract (arXiv)

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.