<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jian-etal-2025-cyut">
    <titleInfo>
        <title>CYUT-NLP at ROCLING-2025 Shared Task: Valence–Arousal Prediction in Physicians’ Texts Using BERT, RAG, and Multi-Teacher Pseudo-Labeling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yi-Min</namePart>
        <namePart type="family">Jian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">An</namePart>
        <namePart type="given">Yu</namePart>
        <namePart type="family">Hsiao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shih-Hung</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kai-Wei</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ke-Han</namePart>
            <namePart type="family">Lu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chih-Kai</namePart>
            <namePart type="family">Yang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zhi-Rui</namePart>
            <namePart type="family">Tam</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wen-Yu</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chung-Che</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">National Taiwan University, Taipei City, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-379-1</identifier>
    </relatedItem>
    <abstract>Accurately modeling physicians’ emotional states from self-reflection texts remains challenging due to the lowresource, domain-specific nature of medical corpora. The proposed workflow performs Retrieval-Augmented Generation (RAG) and multi-teacher pseudo-labeling to generate high-quality augmented data. This workflow enables effective crossdomain adaptation from general text corpora to professional medical texts. Evaluations on the ROCLING 2025 test set demonstrate substantial improvements over the best-performing baseline in Valence–Arousal prediction accuracy and model stability. Importantly, the workflow is domain-agnostic and provides a generalizable methodology for systematically transferring models to new, low-resource domains, making it applicable beyond medical text analysis.</abstract>
    <identifier type="citekey">jian-etal-2025-cyut</identifier>
    <location>
        <url>https://aclanthology.org/2025.rocling-main.42/</url>
    </location>
    <part>
        <date>2025-11</date>
        <extent unit="page">
            <start>381</start>
            <end>389</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CYUT-NLP at ROCLING-2025 Shared Task: Valence–Arousal Prediction in Physicians’ Texts Using BERT, RAG, and Multi-Teacher Pseudo-Labeling
%A Jian, Yi-Min
%A Hsiao, An Yu
%A Wu, Shih-Hung
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F jian-etal-2025-cyut
%X Accurately modeling physicians’ emotional states from self-reflection texts remains challenging due to the lowresource, domain-specific nature of medical corpora. The proposed workflow performs Retrieval-Augmented Generation (RAG) and multi-teacher pseudo-labeling to generate high-quality augmented data. This workflow enables effective crossdomain adaptation from general text corpora to professional medical texts. Evaluations on the ROCLING 2025 test set demonstrate substantial improvements over the best-performing baseline in Valence–Arousal prediction accuracy and model stability. Importantly, the workflow is domain-agnostic and provides a generalizable methodology for systematically transferring models to new, low-resource domains, making it applicable beyond medical text analysis.
%U https://aclanthology.org/2025.rocling-main.42/
%P 381-389

Markdown (Informal)

[CYUT-NLP at ROCLING-2025 Shared Task: Valence–Arousal Prediction in Physicians’ Texts Using BERT, RAG, and Multi-Teacher Pseudo-Labeling](https://aclanthology.org/2025.rocling-main.42/) (Jian et al., ROCLING 2025)

• CYUT-NLP at ROCLING-2025 Shared Task: Valence–Arousal Prediction in Physicians’ Texts Using BERT, RAG, and Multi-Teacher Pseudo-Labeling (Jian et al., ROCLING 2025)

ACL

• Yi-Min Jian, An Yu Hsiao, and Shih-Hung Wu. 2025. CYUT-NLP at ROCLING-2025 Shared Task: Valence–Arousal Prediction in Physicians’ Texts Using BERT, RAG, and Multi-Teacher Pseudo-Labeling. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 381–389, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.