@inproceedings{na-etal-2026-never,
title = "You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations",
author = "Na, Hongbin and
Wang, Zimu and
Chen, Zhaoming and
Zhou, Peilin and
Hua, Yining and
Zhou, Grace Ziqi and
Zhang, Haiyang and
Shen, Tao and
Wang, Wei and
Torous, John and
Ji, Shaoxiong and
Chen, Ling",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.708/",
pages = "14428--14448",
ISBN = "979-8-89176-395-1",
abstract = "Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid use or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4,709 utterances, including 2,336 help seeker turns, with double-blind labeling reaching Cohen{'}s kappa of 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 24.0{\%}. In expert review, it averaged 4.62 for evidence supportiveness, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong large language models (LLMs) in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30{\%} and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We release the corpus, annotations, code, and prompts to support research on defensive functioning in language."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="na-etal-2026-never">
<titleInfo>
<title>You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hongbin</namePart>
<namePart type="family">Na</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zimu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhaoming</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peilin</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yining</namePart>
<namePart type="family">Hua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Grace</namePart>
<namePart type="given">Ziqi</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haiyang</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tao</namePart>
<namePart type="family">Shen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">John</namePart>
<namePart type="family">Torous</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shaoxiong</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ling</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid use or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4,709 utterances, including 2,336 help seeker turns, with double-blind labeling reaching Cohen’s kappa of 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 24.0%. In expert review, it averaged 4.62 for evidence supportiveness, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong large language models (LLMs) in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We release the corpus, annotations, code, and prompts to support research on defensive functioning in language.</abstract>
<identifier type="citekey">na-etal-2026-never</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.708/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>14428</start>
<end>14448</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
%A Na, Hongbin
%A Wang, Zimu
%A Chen, Zhaoming
%A Zhou, Peilin
%A Hua, Yining
%A Zhou, Grace Ziqi
%A Zhang, Haiyang
%A Shen, Tao
%A Wang, Wei
%A Torous, John
%A Ji, Shaoxiong
%A Chen, Ling
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F na-etal-2026-never
%X Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid use or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4,709 utterances, including 2,336 help seeker turns, with double-blind labeling reaching Cohen’s kappa of 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 24.0%. In expert review, it averaged 4.62 for evidence supportiveness, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong large language models (LLMs) in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We release the corpus, annotations, code, and prompts to support research on defensive functioning in language.
%U https://aclanthology.org/2026.findings-acl.708/
%P 14428-14448
Markdown (Informal)
[You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations](https://aclanthology.org/2026.findings-acl.708/) (Na et al., Findings 2026)
ACL
- Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, and Ling Chen. 2026. You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14428–14448, San Diego, California, United States. Association for Computational Linguistics.