<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dash-etal-2026-semantic">
    <titleInfo>
        <title>Semantic Vectors at SemEval-2026 Task 9: Robust Multilingual Polarization Detection via Dual-Encoder Fusion and Expert Ensembling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ankit</namePart>
        <namePart type="family">Dash</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Priyanshu</namePart>
        <namePart type="family">Mittal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Piyush</namePart>
        <namePart type="family">Prashant</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sunil</namePart>
        <namePart type="family">Saumya</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 present SEMANTIC VECTORS, our system for POLAR@SemEval-2026 Task 9 on multilingual online polarization detection across 22 typologically diverse languages. Polarization is frequently conveyed through implicit rhetorical framing, making cross-lingual detection highly challenging. We address this with a Siamese dual-encoder jointly fine-tuning mDeBERTa-v3-base and XLM-ROBERTa-large via 4-bit QLoRA, fused with language-specific expert models (GBERT, Italian BERT, Swahili BERT) through an XGBoost meta-stacker with per-language Platt calibration. Rather than addressing class imbalance, focal loss functions as a hard-example miner, concentrating gradients on subtly framed instances rather than lexically obvious ones. Combined with per-language threshold optimization, our system achieves macro-F1=0.797 and accuracy=0.827 across all 22 languages.</abstract>
    <identifier type="citekey">dash-etal-2026-semantic</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.239/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>1903</start>
            <end>1910</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Semantic Vectors at SemEval-2026 Task 9: Robust Multilingual Polarization Detection via Dual-Encoder Fusion and Expert Ensembling
%A Dash, Ankit
%A Mittal, Priyanshu
%A Prashant, Piyush
%A Saumya, Sunil
%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 dash-etal-2026-semantic
%X We present SEMANTIC VECTORS, our system for POLAR@SemEval-2026 Task 9 on multilingual online polarization detection across 22 typologically diverse languages. Polarization is frequently conveyed through implicit rhetorical framing, making cross-lingual detection highly challenging. We address this with a Siamese dual-encoder jointly fine-tuning mDeBERTa-v3-base and XLM-ROBERTa-large via 4-bit QLoRA, fused with language-specific expert models (GBERT, Italian BERT, Swahili BERT) through an XGBoost meta-stacker with per-language Platt calibration. Rather than addressing class imbalance, focal loss functions as a hard-example miner, concentrating gradients on subtly framed instances rather than lexically obvious ones. Combined with per-language threshold optimization, our system achieves macro-F1=0.797 and accuracy=0.827 across all 22 languages.
%U https://aclanthology.org/2026.semeval-1.239/
%P 1903-1910

Markdown (Informal)

[Semantic Vectors at SemEval-2026 Task 9: Robust Multilingual Polarization Detection via Dual-Encoder Fusion and Expert Ensembling](https://aclanthology.org/2026.semeval-1.239/) (Dash et al., SemEval 2026)

• Semantic Vectors at SemEval-2026 Task 9: Robust Multilingual Polarization Detection via Dual-Encoder Fusion and Expert Ensembling (Dash et al., SemEval 2026)

ACL

• Ankit Dash, Priyanshu Mittal, Piyush Prashant, and Sunil Saumya. 2026. Semantic Vectors at SemEval-2026 Task 9: Robust Multilingual Polarization Detection via Dual-Encoder Fusion and Expert Ensembling. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1903–1910, San Diego, California, USA. Association for Computational Linguistics.