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Found 13 papers, 3 papers with code
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios
In psychotherapy interactions, the quality of a session is assessed by codifying the communicative behaviors of participants during the conversation through manual observation and annotation.
Local dynamic mode of Cognitive Behavioral Therapy
In order to increase mental health equity among the most vulnerable and marginalized communities, it is important to increase access to high-quality therapists.
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text.
An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates
In particular, we model the global quality as a linear function of the local quality scores, which allows us to update the segment-level quality estimates based on the session-level quality prediction.
Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations
In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction.
Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies
With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services.
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context.
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.
Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions
We propose a novel method to augment the word-based features with the utterance level tags for subsequent CBT code estimation.
A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification
The DAN-LPE simultaneously trains a domain adversarial net and processes label proportions estimation by the confusion of the source domain and the predictions of the target domain.
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
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.
Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance
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.
Multi-label Multi-task Deep Learning for Behavioral Coding
We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms.
