<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pedersen-2026-duluth">
    <titleInfo>
        <title>Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ted</namePart>
        <namePart type="family">Pedersen</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 20th International Workshop on Semantic Evaluation (2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</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">979-8-89176-414-9</identifier>
    </relatedItem>
    <abstract>This paper presents the Duluth approach toSemEval-2026 Task 6 on CLARITY: Unmask-ing Political Question Evasions. We addressTask 1 (clarity-level classification) and Task 2(evasion-level classification), both of which in-volve classifying question–answer pairs fromU.S. presidential interviews using a two-leveltaxonomy of response clarity. Our system isbased on DeBERTa-V3-base, extended withfocal loss, layer-wise learning rate decay, andboolean discourse features. To address classimbalance in the training data, we augmentminority classes using synthetic examples gen-erated by Gemini 3 and Claude Sonnet 4.5. Ourbest configuration achieved a Macro F1 of 0.76on the Task 1 evaluation set, placing 8th outof 40 teams. The top-ranked system (TeleAI)achieved 0.89, while the mean score across par-ticipants was 0.70. Error analysis reveals thatthe dominant source of misclassification is con-fusion between Ambivalent and Clear Replyresponses, a pattern that mirrors disagreementsamong human annotators. Our findings demon-strate that LLM-based data augmentation canmeaningfully improve minority-class recall onnuanced political discourse tasks.</abstract>
    <identifier type="citekey">pedersen-2026-duluth</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.94/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>650</start>
            <end>657</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions
%A Pedersen, Ted
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F pedersen-2026-duluth
%X This paper presents the Duluth approach toSemEval-2026 Task 6 on CLARITY: Unmask-ing Political Question Evasions. We addressTask 1 (clarity-level classification) and Task 2(evasion-level classification), both of which in-volve classifying question–answer pairs fromU.S. presidential interviews using a two-leveltaxonomy of response clarity. Our system isbased on DeBERTa-V3-base, extended withfocal loss, layer-wise learning rate decay, andboolean discourse features. To address classimbalance in the training data, we augmentminority classes using synthetic examples gen-erated by Gemini 3 and Claude Sonnet 4.5. Ourbest configuration achieved a Macro F1 of 0.76on the Task 1 evaluation set, placing 8th outof 40 teams. The top-ranked system (TeleAI)achieved 0.89, while the mean score across par-ticipants was 0.70. Error analysis reveals thatthe dominant source of misclassification is con-fusion between Ambivalent and Clear Replyresponses, a pattern that mirrors disagreementsamong human annotators. Our findings demon-strate that LLM-based data augmentation canmeaningfully improve minority-class recall onnuanced political discourse tasks.
%U https://aclanthology.org/2026.semeval-1.94/
%P 650-657

Markdown (Informal)

[Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions](https://aclanthology.org/2026.semeval-1.94/) (Pedersen, SemEval 2026)

• Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions (Pedersen, SemEval 2026)

ACL

• Ted Pedersen. 2026. Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 650–657, San Diego, California, USA. Association for Computational Linguistics.