Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.
Columbia University（哥伦比亚大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
As a predictive measure of the treatment outcome in psychotherapy, the working alliance measures the agreement of the patient and the therapist in terms of their bond, task and goal. Long been a clinical quantity estimated by the patients' and therapists' self-evaluative reports, we believe that the working alliance can be better characterized using natural language processing technique directly in the dialogue transcribed in each therapy session. In this work, we propose the Working Alliance Transformer (WAT), a Transformer-based classification model that has a psychological state encoder whi…
How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues
College of Information Sciences and Technology, Penn State University（宾夕法尼亚州立大学信息科学与技术学院）；Department of Psychiatry and Behavioral Sciences, Emory University（埃默里大学精神病学与行为科学系）；School of Interactive Computing, Georgia Tech（佐治亚理工学院交互计算学院）；School of Psychology, Georgia Tech（佐治亚理工学院心理学学院）
Columbia University（哥伦比亚大学）；IBM TJ Watson Research Center（IBM托马斯·J·沃森研究中心）
We present the TherapyView, a demonstration system to help therapists visualize the dynamic contents of past treatment sessions, enabled by the state-of-the-art neural topic modeling techniques to analyze the topical tendencies of various psychiatric conditions and deep learning-based image generation engine to provide a visual summary. The system incorporates temporal modeling to provide a time-series representation of topic similarities at a turn-level resolution and AI-generated artworks given the dialogue segments to provide a concise representations of the contents covered in the session,…
TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
Department of Computer Science, Stanford University, Stanford, CA, USA（计算机科学系，斯坦福大学，斯坦福，加州，美国）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学，斯坦福，加州，美国）；Department of Biomedical Data Science, Stanford University, Stanford, CA, USA（生物医学数据科学系，斯坦福大学，斯坦福，加州，美国）；University of California, Berkeley, Berkeley, CA, USA（加州大学伯克利分校，伯克利，加州，美国）；The University of Hong Kong, Hong Kong（香港大学，香港）
University of Denver（丹佛大学）；Eaton Senior Community（伊顿老年社区）
Social robots are becoming an integrated part of our daily life due to their ability to provide companionship and entertainment. A subfield of robotics, Socially Assistive Robotics (SAR), is particularly suitable for expanding these benefits into the healthcare setting because of its unique ability to provide cognitive, social, and emotional support. This paper presents our recent research on developing SAR by evaluating the ability of a life-like conversational social robot, called Ryan, to administer internet-delivered cognitive behavioral therapy (iCBT) to older adults with depression. For …
Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions
Department of Computer Science, Shantou University（汕头大学计算机科学系）；Department of Automation, Tsinghua University（清华大学自动化系）；BNRIST, Tsinghua University（清华大学脑科学与类脑智能研究院）
Are Large Language Models Possible to Conduct Cognitive Behavioral Therapy?
School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）
Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；Goethe University（歌德大学）；University Hospital Frankfurt（法兰克福大学医院）
According to the Global Burden of Disease list provided by the World Health Organization (WHO), mental disorders are among the most debilitating disorders.To improve the diagnosis and the therapy effectiveness in recent years, researchers have tried to identify individual biomarkers. Gathering neurobiological data however, is costly and time-consuming. Another potential source of information, which is already part of the clinical routine, are therapist-patient dialogues. While there are some pioneering works investigating the role of language as predictors for various therapeutic parameters, f…
BITS Pilani（比尔拉理工学院）；Maharaja Agrasen Institute of Technology（马哈拉贾·阿格拉森理工学院）；IIIT-Delhi（德里印度信息技术学院）
The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an a…
University of Southern California（南加州大学）；Interactions LLC（Interactions有限责任公司）；University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）
Text-based computational approaches for assessing the quality of psychotherapy are being developed to support quality assurance and clinical training. However, due to the long durations of typical conversation based therapy sessions, and due to limited annotated modeling resources, computational methods largely rely on frequency-based lexical features or dialogue acts to assess the overall session level characteristics. In this work, we propose a hierarchical framework to automatically evaluate the quality of transcribed Cognitive Behavioral Therapy (CBT) interactions. Given the richly dynamic…
