Papers by Kenneth Resnicow
PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing (2022.emnlp-main)
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| Challenge: | Existing approaches to provide constructive feedback to counselors are limited by the time and cost involved. |
| Approach: | They propose a system that takes as input a client prompt and a counselor response and outputs a score indicating the level of reflection in the counselor response. |
| Outcome: | The proposed model outperforms baselines on different metrics and can be used to provide useful feedback to counseling trainees. |
Exploring Self-Identified Counseling Expertise in Online Support Forums (2021.findings-acl)
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Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
| Challenge: | Increasing number of people engage in online health forums, making it important to understand the quality of the advice they receive. |
| Approach: | They examine the role of expertise in responses to help-seeking posts . they find that a classifier can distinguish between peer and self-identified mental health professionals' interactions . |
| Outcome: | The findings show that experts' language use differs between groups, and that their comments engage the support-seeker further. |
What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations (P19-1)
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| Challenge: | Qualitative counseling relies on active collaboration between clients and counselors . |
| Approach: | They propose to use linguistic features to capture differences between high- and low-quality counseling conversations to build automatic classifiers that can predict counseling quality with accuracies of up to 88%. |
| Outcome: | The proposed model can predict counseling quality with accuracies of up to 88%. |
Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling (L18-1)
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| Challenge: | Behavioral and mental health disorders are the most costly and prevalent conditions worldwide. |
| Approach: | They propose to use a dataset to analyze counseling interactions by using aspects such as mirroring, empathy, and reflective listening to build text-based classifiers. |
| Outcome: | The proposed dataset can be used to build text-based classifiers able to predict the overall quality of a counseling conversation and provide insights into the linguistic differences between low-quality and high-quality counseling. |