Dana Atzil-Slonim


2024

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Overview of the CLPsych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts
Jenny Chim | Adam Tsakalidis | Dimitris Gkoumas | Dana Atzil-Slonim | Yaakov Ophir | Ayah Zirikly | Philip Resnik | Maria Liakata
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level (‘Low’, ‘Mod- erate’ or ‘High’) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.

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Predicting Client Emotions and Therapist Interventions in Psychotherapy Dialogues
Tobias Mayer | Neha Warikoo | Amir Eliassaf | Dana Atzil-Slonim | Iryna Gurevych
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing (NLP) can advance psychotherapy research by scaling up therapy dialogue analysis as well as by allowing researchers to examine client-therapist interactions in detail. Previous studies have mainly either explored the clients’ behavior or the therapists’ intervention in dialogues. Yet, modelling conversations from both dialogue participants is crucial to understanding the therapeutic interaction. This study explores speaker contribution-based dialogue acts at the utterance-level; i.e, the therapist - Intervention Prediction (IP) and the client - Emotion Recognition (ER) in psychotherapy using a pan-theoretical schema. We perform experiments with fine-tuned language models and light-weight adapter solutions on a Hebrew dataset. We deploy the results from our ER model predictions in investigating the coherence between client self-reports on emotion and the utterance-level emotions. Our best adapters achieved on-par performance with fully fine-tuned models, at 0.64 and 0.66 micro F1 for IP and ER, respectively. In addition, our analysis identifies ambiguities within categorical clinical coding, which can be used to fine-tune the coding schema. Finally, our results indicate a positive correlation between client self-reports and utterance-level emotions.

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Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media
Jiayu Song | Jenny Chim | Adam Tsakalidis | Julia Ive | Dana Atzil-Slonim | Maria Liakata
Findings of the Association for Computational Linguistics ACL 2024

We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) a temporally sensitive abstractive summary of the user’s timeline in first person, generated by a novel hierarchical variational autoencoder, TH-VAE. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

2022

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Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Ayah Zirikly | Dana Atzil-Slonim | Maria Liakata | Steven Bedrick | Bart Desmet | Molly Ireland | Andrew Lee | Sean MacAvaney | Matthew Purver | Rebecca Resnik | Andrew Yates
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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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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Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
Adam Tsakalidis | Jenny Chim | Iman Munire Bilal | Ayah Zirikly | Dana Atzil-Slonim | Federico Nanni | Philip Resnik | Manas Gaur | Kaushik Roy | Becky Inkster | Jeff Leintz | Maria Liakata
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-‘Switches’- and gradual changes -‘Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).

2021

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Hebrew Psychological Lexicons
Natalie Shapira | Dana Atzil-Slonim | Daniel Juravski | Moran Baruch | Dana Stolowicz-Melman | Adar Paz | Tal Alfi-Yogev | Roy Azoulay | Adi Singer | Maayan Revivo | Chen Dahbash | Limor Dayan | Tamar Naim | Lidar Gez | Boaz Yanai | Adva Maman | Adam Nadaf | Elinor Sarfati | Amna Baloum | Tal Naor | Ephraim Mosenkis | Badreya Sarsour | Jany Gelfand Morgenshteyn | Yarden Elias | Liat Braun | Moria Rubin | Matan Kenigsbuch | Noa Bergwerk | Noam Yosef | Sivan Peled | Coral Avigdor | Rahav Obercyger | Rachel Mann | Tomer Alper | Inbal Beka | Ori Shapira | Yoav Goldberg
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

We introduce a large set of Hebrew lexicons pertaining to psychological aspects. These lexicons are useful for various psychology applications such as detecting emotional state, well being, relationship quality in conversation, identifying topics (e.g., family, work) and many more. We discuss the challenges in creating and validating lexicons in a new language, and highlight our methodological considerations in the data-driven lexicon construction process. Most of the lexicons are publicly available, which will facilitate further research on Hebrew clinical psychology text analysis. The lexicons were developed through data driven means, and verified by domain experts, clinical psychologists and psychology students, in a process of reconciliation with three judges. Development and verification relied on a dataset of a total of 872 psychotherapy session transcripts. We describe the construction process of each collection, the final resource and initial results of research studies employing this resource.

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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.