@inproceedings{huang-etal-2026-zzucs,
title = "zzucs at {P}sy{D}ef{D}etect: Bridging Long-Tail Imbalance and Clinical Rubrics for {DMRS} Defense-Level Detection",
author = "Huang, Bin and
Su, Liuyuan and
Yuan, Kaixuan and
Zhao, Guanghui and
Zhang, Shixin and
Zhang, Kunli",
editor = "Gupta, Deepak and
Demner-Fushman, Dina",
booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-2.3/",
pages = "13--23",
ISBN = "979-8-89176-435-4",
abstract = "Detecting DMRS defense levels in emotionalsupport dialogues is challenging due to severe class imbalance and fine-grained clinical distinctions between adjacent levels, issueswell documented in psychotherapy-orientedNLP surveys (Na et al., 2025). We presentzzucs for PsyDefDetect at BioNLP 2026 (Naet al., 2026a), adopting a data{--}supervisionco-design strategy. SCCR applies stratifiedresampling to balance support across nine defense levels. CoR{--}QLoRA encodes clinical rubrics, including task contracts, taxonomy definitions, and boundary cues, into staticprompts for 8B model fine-tuning. Ablationsshow SCCR improves macro-F1 by 4.9 pointsover random oversampling. Our system fromteam zzucs, submitted on CodaBench underthe display name sly{\_}zzu with submission ID652647, achieves 0.3585 macro-F1 on the official blind-test leaderboard LB1. It ranks6th of 21 registered teams with official submissions and surpasses all published 8B baselines by 4.4 F1 points over the strongest 8Bcomparator, Ministral-8B. The code has beenreleased at https://github.com/jackssdd/zzucs{\_}psydefdetect{\_}code."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="huang-etal-2026-zzucs">
<titleInfo>
<title>zzucs at PsyDefDetect: Bridging Long-Tail Imbalance and Clinical Rubrics for DMRS Defense-Level Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bin</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liuyuan</namePart>
<namePart type="family">Su</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kaixuan</namePart>
<namePart type="family">Yuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guanghui</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shixin</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kunli</namePart>
<namePart type="family">Zhang</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>Proceedings of the BioNLP 2026 (Shared Tasks)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Deepak</namePart>
<namePart type="family">Gupta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dina</namePart>
<namePart type="family">Demner-Fushman</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, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-435-4</identifier>
</relatedItem>
<abstract>Detecting DMRS defense levels in emotionalsupport dialogues is challenging due to severe class imbalance and fine-grained clinical distinctions between adjacent levels, issueswell documented in psychotherapy-orientedNLP surveys (Na et al., 2025). We presentzzucs for PsyDefDetect at BioNLP 2026 (Naet al., 2026a), adopting a data–supervisionco-design strategy. SCCR applies stratifiedresampling to balance support across nine defense levels. CoR–QLoRA encodes clinical rubrics, including task contracts, taxonomy definitions, and boundary cues, into staticprompts for 8B model fine-tuning. Ablationsshow SCCR improves macro-F1 by 4.9 pointsover random oversampling. Our system fromteam zzucs, submitted on CodaBench underthe display name sly_zzu with submission ID652647, achieves 0.3585 macro-F1 on the official blind-test leaderboard LB1. It ranks6th of 21 registered teams with official submissions and surpasses all published 8B baselines by 4.4 F1 points over the strongest 8Bcomparator, Ministral-8B. The code has beenreleased at https://github.com/jackssdd/zzucs_psydefdetect_code.</abstract>
<identifier type="citekey">huang-etal-2026-zzucs</identifier>
<location>
<url>https://aclanthology.org/2026.bionlp-2.3/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>13</start>
<end>23</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T zzucs at PsyDefDetect: Bridging Long-Tail Imbalance and Clinical Rubrics for DMRS Defense-Level Detection
%A Huang, Bin
%A Su, Liuyuan
%A Yuan, Kaixuan
%A Zhao, Guanghui
%A Zhang, Shixin
%A Zhang, Kunli
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F huang-etal-2026-zzucs
%X Detecting DMRS defense levels in emotionalsupport dialogues is challenging due to severe class imbalance and fine-grained clinical distinctions between adjacent levels, issueswell documented in psychotherapy-orientedNLP surveys (Na et al., 2025). We presentzzucs for PsyDefDetect at BioNLP 2026 (Naet al., 2026a), adopting a data–supervisionco-design strategy. SCCR applies stratifiedresampling to balance support across nine defense levels. CoR–QLoRA encodes clinical rubrics, including task contracts, taxonomy definitions, and boundary cues, into staticprompts for 8B model fine-tuning. Ablationsshow SCCR improves macro-F1 by 4.9 pointsover random oversampling. Our system fromteam zzucs, submitted on CodaBench underthe display name sly_zzu with submission ID652647, achieves 0.3585 macro-F1 on the official blind-test leaderboard LB1. It ranks6th of 21 registered teams with official submissions and surpasses all published 8B baselines by 4.4 F1 points over the strongest 8Bcomparator, Ministral-8B. The code has beenreleased at https://github.com/jackssdd/zzucs_psydefdetect_code.
%U https://aclanthology.org/2026.bionlp-2.3/
%P 13-23
Markdown (Informal)
[zzucs at PsyDefDetect: Bridging Long-Tail Imbalance and Clinical Rubrics for DMRS Defense-Level Detection](https://aclanthology.org/2026.bionlp-2.3/) (Huang et al., BioNLP 2026)
ACL