2 research outputs found
Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance
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
An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates
Computational approaches for assessing the quality of conversation-based
psychotherapy, such as Cognitive Behavioral Therapy (CBT) and Motivational
Interviewing (MI), have been developed recently to support quality assurance
and clinical training. However, due to the long session lengths and limited
modeling resources, computational methods largely rely on frequency-based
lexical features or distribution of dialogue acts. In this work, we propose a
hierarchical framework to automatically evaluate the quality of a CBT
interaction. We divide each psychotherapy session into conversation segments
and input those into a BERT-based model to produce segment embeddings. We first
fine-tune BERT for predicting segment-level (local) quality scores and then use
segment embeddings as lower-level input to a Bidirectional LSTM-based neural
network to predict session-level (global) quality estimates. In particular, the
segment-level quality scores are initialized with the session-level scores and
we model the global quality as a function of the local quality scores to
achieve the accurate segment-level quality estimates. These estimated
segment-level scores benefit theBERT fine-tuning and in learning better segment
embeddings. We evaluate the proposed framework on data drawn from real-world
CBT clinical session recordings to predict multiple session-level behavior
codes. The results indicate that our approach leads to improved evaluation
accuracy for most codes in both regression and classification tasks