Rivka Tuval-Mashiach

Also published as: Rivka Tuval Mashiach


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Measuring Linguistic Synchrony in Psychotherapy
Natalie Shapira | Dana Atzil-Slonim | Rivka Tuval Mashiach | Ori Shapira
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

We study the phenomenon of linguistic synchrony between clients and therapists in a psychotherapy process. Linguistic Synchrony (LS) can be viewed as any observed interdependence or association between more than one person?s linguistic behavior. Accordingly, we establish LS as a methodological task. We suggest a LS function that applies a linguistic similarity measure based on the Jensen-Shannon distance across the observed part-of-speech tag distributions (JSDuPos) of the speakers in different time frames. We perform a study over a unique corpus of 872 transcribed sessions, covering 68 clients and 59 therapists. After establishing the presence of client-therapist LS, we verify its association with therapeutic alliance and treatment outcome (measured using WAI and ORS), and additionally analyse the behavior of JSDuPos throughout treatment. Results indicate that (1) higher linguistic similarity at the session level associates with higher therapeutic alliance as reported by the client and therapist at the end of the session, (2) higher linguistic similarity at the session level associates with higher level of treatment outcome as reported by the client at the beginnings of the next sessions, (3) there is a significant linear increase in linguistic similarity throughout treatment, (4) surprisingly, higher LS associates with lower treatment outcome. Finally, we demonstrate how the LS function can be used to interpret and explore the mechanism for synchrony.


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Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions
Adam Tsakalidis | Dana Atzil-Slonim | Asaf Polakovski | Natalie Shapira | Rivka Tuval-Mashiach | Maria Liakata
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients. Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.