David Atkins


2020

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Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations
Karan Singla | Zhuohao Chen | David Atkins | Shrikanth Narayanan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Spoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR). We propose a novel framework for predicting utterance level labels directly from speech features, thus removing the dependency on first generating transcripts, and transcription free behavioral coding. Our classifier uses a pretrained Speech-2-Vector encoder as bottleneck to generate word-level representations from speech features. This pretrained encoder learns to encode speech features for a word using an objective similar to Word2Vec. Our proposed approach just uses speech features and word segmentation information for predicting spoken utterance-level target labels. We show that our model achieves competitive results to other state-of-the-art approaches which use transcribed text for the task of predicting psychotherapy-relevant behavior codes.

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A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
Ashish Sharma | Adam Miner | David Atkins | Tim Althoff
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. Because millions of people use text-based platforms for mental health support, understanding empathy in these contexts is crucial. In this work, we present a computational approach to understanding how empathy is expressed in online mental health platforms. We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations. We collect and share a corpus of 10k (post, response) pairs annotated using this empathy framework with supporting evidence for annotations (rationales). We develop a multi-task RoBERTa-based bi-encoder model for identifying empathy in conversations and extracting rationales underlying its predictions. Experiments demonstrate that our approach can effectively identify empathic conversations. We further apply this model to analyze 235k mental health interactions and show that users do not self-learn empathy over time, revealing opportunities for empathy training and feedback.

2019

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Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
Jie Cao | Michael Tanana | Zac Imel | Eric Poitras | David Atkins | Vivek Srikumar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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.

2016

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Is Sentiment in Movies the Same as Sentiment in Psychotherapy? Comparisons Using a New Psychotherapy Sentiment Database
Michael Tanana | Aaron Dembe | Christina S. Soma | Zac Imel | David Atkins | Vivek Srikumar
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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Social and linguistic behavior and its correlation to trait empathy
Marina Litvak | Jahna Otterbacher | Chee Siang Ang | David Atkins
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

A growing body of research exploits social media behaviors to gauge psychological character-istics, though trait empathy has received little attention. Because of its intimate link to the abil-ity to relate to others, our research aims to predict participants’ levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari-ance explained in Davis’ Interpersonal Reactivity Index (IRI) scores on four constructs (em-pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy’s role in facilitating interactions on social media.

2015

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Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing
Michael Tanana | Kevin Hallgren | Zac Imel | David Atkins | Padhraic Smyth | Vivek Srikumar
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality