<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="khan-2026-su">
    <titleInfo>
        <title>SU NLP 29 at SemEval-2026 Task 5: DynaOrd - Hybrid Dynamic Ordinal Regression with LoRA-Fine-Tuned DeBERTa-v3</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Musab</namePart>
        <namePart type="family">Khan</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>We describe our system submitted to SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous sentences within narrative contexts. The task requires predicting human-perceived plausibility scores on a 1-5 scale for candidate word meanings embedded in short stories, posing challenges such as limited training data and the ordinal nature of target labels. Our approach combines a DeBERTa-v3-large encoder with Low-Rank Adaptation (LoRA) and a dynamically weighted hybrid CORAL-MSE loss for ordinal regression. This formulation adapts the contribution of ranking and regression objectives during training, prioritizing ordinal consistency early and regression refinement in later epochs.We analyze the contributions of dynamic loss weighting to overall system performance.</abstract>
    <identifier type="citekey">khan-2026-su</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.74/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>514</start>
            <end>521</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SU NLP 29 at SemEval-2026 Task 5: DynaOrd - Hybrid Dynamic Ordinal Regression with LoRA-Fine-Tuned DeBERTa-v3
%A Khan, Musab
%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 khan-2026-su
%X We describe our system submitted to SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous sentences within narrative contexts. The task requires predicting human-perceived plausibility scores on a 1-5 scale for candidate word meanings embedded in short stories, posing challenges such as limited training data and the ordinal nature of target labels. Our approach combines a DeBERTa-v3-large encoder with Low-Rank Adaptation (LoRA) and a dynamically weighted hybrid CORAL-MSE loss for ordinal regression. This formulation adapts the contribution of ranking and regression objectives during training, prioritizing ordinal consistency early and regression refinement in later epochs.We analyze the contributions of dynamic loss weighting to overall system performance.
%U https://aclanthology.org/2026.semeval-1.74/
%P 514-521

Markdown (Informal)

[SU NLP 29 at SemEval-2026 Task 5: DynaOrd - Hybrid Dynamic Ordinal Regression with LoRA-Fine-Tuned DeBERTa-v3](https://aclanthology.org/2026.semeval-1.74/) (Khan, SemEval 2026)

• SU NLP 29 at SemEval-2026 Task 5: DynaOrd - Hybrid Dynamic Ordinal Regression with LoRA-Fine-Tuned DeBERTa-v3 (Khan, SemEval 2026)

ACL

• Musab Khan. 2026. SU NLP 29 at SemEval-2026 Task 5: DynaOrd - Hybrid Dynamic Ordinal Regression with LoRA-Fine-Tuned DeBERTa-v3. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 514–521, San Diego, California, USA. Association for Computational Linguistics.