Search Results for author: David C. Atkins

Found 10 papers, 3 papers with code

An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates

no code implementations15 Jun 2021 Zhuohao Chen, Nikolaos Flemotomos, Karan Singla, Torrey A. Creed, David C. Atkins, Shrikanth Narayanan

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.

Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations

no code implementations23 Feb 2021 Nikolaos Flemotomos, Victor R. Martinez, Zhuohao Chen, Torrey A. Creed, David C. Atkins, Shrikanth Narayanan

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

no code implementations22 Feb 2021 Nikolaos Flemotomos, Victor R. Martinez, Zhuohao Chen, Karan Singla, Victor Ardulov, Raghuveer Peri, Derek D. Caperton, James Gibson, Michael J. Tanana, Panayiotis Georgiou, Jake Van Epps, Sarah P. Lord, Tad Hirsch, Zac E. Imel, David C. Atkins, Shrikanth Narayanan

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

1 code implementation19 Jan 2021 ASHISH SHARMA, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff

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.

Dialogue Generation Language Modelling +1

A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support

2 code implementations EMNLP 2020 Ashish Sharma, Adam S. Miner, David C. Atkins, Tim Althoff

We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.

A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification

no code implementations16 Mar 2020 Zhuohao Chen, Singla Karan, David C. Atkins, Zac E. Imel, Shrikanth Narayanan

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.

General Classification Text Classification +1

Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

1 code implementation ACL 2019 Jie Cao, Michael Tanana, Zac E. Imel, Eric Poitras, David C. Atkins, Vivek Srikumar

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

no code implementations12 Apr 2019 Md Nasir, Sandeep Nallan Chakravarthula, Brian Baucom, David C. Atkins, Panayiotis Georgiou, Shrikanth Narayanan

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

no code implementations29 Oct 2018 James Gibson, David C. Atkins, Torrey Creed, Zac Imel, Panayiotis Georgiou, Shrikanth Narayanan

We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms.

Multi-Task Learning

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