@inproceedings{min-etal-2022-pair,
title = "{PAIR}: Prompt-Aware marg{I}n Ranking for Counselor Reflection Scoring in Motivational Interviewing",
author = "Min, Do June and
P{\'e}rez-Rosas, Ver{\'o}nica and
Resnicow, Kenneth and
Mihalcea, Rada",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.11",
pages = "148--158",
abstract = "Counselor reflection is a core verbal skill used by mental health counselors to express understanding and affirmation of the client{'}s experience and concerns. In this paper, we propose a system for the analysis of counselor reflections. Specifically, our system takes as input one dialog turn containing a client prompt and a counselor response, and outputs a score indicating the level of reflection in the counselor response. We compile a dataset consisting of different levels of reflective listening skills, and propose the Prompt-Aware margIn Ranking (PAIR) framework that contrasts positive and negative prompt and response pairs using specially designed multi-gap and prompt-aware margin ranking losses. Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our analysis model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="min-etal-2022-pair">
<titleInfo>
<title>PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Do</namePart>
<namePart type="given">June</namePart>
<namePart type="family">Min</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Verónica</namePart>
<namePart type="family">Pérez-Rosas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kenneth</namePart>
<namePart type="family">Resnicow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rada</namePart>
<namePart type="family">Mihalcea</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Counselor reflection is a core verbal skill used by mental health counselors to express understanding and affirmation of the client’s experience and concerns. In this paper, we propose a system for the analysis of counselor reflections. Specifically, our system takes as input one dialog turn containing a client prompt and a counselor response, and outputs a score indicating the level of reflection in the counselor response. We compile a dataset consisting of different levels of reflective listening skills, and propose the Prompt-Aware margIn Ranking (PAIR) framework that contrasts positive and negative prompt and response pairs using specially designed multi-gap and prompt-aware margin ranking losses. Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our analysis model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.</abstract>
<identifier type="citekey">min-etal-2022-pair</identifier>
<location>
<url>https://aclanthology.org/2022.emnlp-main.11</url>
</location>
<part>
<date>2022-12</date>
<extent unit="page">
<start>148</start>
<end>158</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing
%A Min, Do June
%A Pérez-Rosas, Verónica
%A Resnicow, Kenneth
%A Mihalcea, Rada
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F min-etal-2022-pair
%X Counselor reflection is a core verbal skill used by mental health counselors to express understanding and affirmation of the client’s experience and concerns. In this paper, we propose a system for the analysis of counselor reflections. Specifically, our system takes as input one dialog turn containing a client prompt and a counselor response, and outputs a score indicating the level of reflection in the counselor response. We compile a dataset consisting of different levels of reflective listening skills, and propose the Prompt-Aware margIn Ranking (PAIR) framework that contrasts positive and negative prompt and response pairs using specially designed multi-gap and prompt-aware margin ranking losses. Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our analysis model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.
%U https://aclanthology.org/2022.emnlp-main.11
%P 148-158
Markdown (Informal)
[PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing](https://aclanthology.org/2022.emnlp-main.11) (Min et al., EMNLP 2022)
ACL