LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation

Le Qiu; Yu-Yin Hsu; Emmanuele Chersoni

LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions.

Anthology ID:: 2026.mwe-1.27
Volume:: Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
Month:: March
Year:: 2026
Address:: Rabat, Marocco
Editors:: Atul Kr. Ojha, Verginica Barbu Mititelu, Mathieu Constant, Ivelina Stoyanova, A. Seza Doğruöz, Alexandre Rademaker
Venues:: MWE | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 203–207
Language:
URL:: https://aclanthology.org/2026.mwe-1.27/
DOI:
Bibkey:
Cite (ACL):: Le Qiu, Yu-Yin Hsu, and Emmanuele Chersoni. 2026. LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation. In Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026), pages 203–207, Rabat, Marocco. Association for Computational Linguistics.
Cite (Informal):: LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation (Qiu et al., MWE 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.mwe-1.27.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{qiu-etal-2026-lst,
    title = "{LST} at {MWE}-2026 {A}d{MIR}e 2: Advancing Multimodal Idiomaticity Representation",
    author = "Qiu, Le  and
      Hsu, Yu-Yin  and
      Chersoni, Emmanuele",
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.mwe-1.27/",
    pages = "203--207",
    ISBN = "979-8-89176-363-0",
    abstract = "This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qiu-etal-2026-lst">
    <titleInfo>
        <title>LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Le</namePart>
        <namePart type="family">Qiu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yu-Yin</namePart>
        <namePart type="family">Hsu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Emmanuele</namePart>
        <namePart type="family">Chersoni</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Verginica</namePart>
            <namePart type="given">Barbu</namePart>
            <namePart type="family">Mititelu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mathieu</namePart>
            <namePart type="family">Constant</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Stoyanova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">A</namePart>
            <namePart type="given">Seza</namePart>
            <namePart type="family">Doğruöz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexandre</namePart>
            <namePart type="family">Rademaker</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Rabat, Marocco</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-363-0</identifier>
    </relatedItem>
    <abstract>This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions.</abstract>
    <identifier type="citekey">qiu-etal-2026-lst</identifier>
    <location>
        <url>https://aclanthology.org/2026.mwe-1.27/</url>
    </location>
    <part>
        <date>2026-03</date>
        <extent unit="page">
            <start>203</start>
            <end>207</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation
%A Qiu, Le
%A Hsu, Yu-Yin
%A Chersoni, Emmanuele
%Y Ojha, Atul Kr.
%Y Mititelu, Verginica Barbu
%Y Constant, Mathieu
%Y Stoyanova, Ivelina
%Y Doğruöz, A. Seza
%Y Rademaker, Alexandre
%S Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-363-0
%F qiu-etal-2026-lst
%X This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions.
%U https://aclanthology.org/2026.mwe-1.27/
%P 203-207

Download as File

Markdown (Informal)

[LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation](https://aclanthology.org/2026.mwe-1.27/) (Qiu et al., MWE 2026)

LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation (Qiu et al., MWE 2026)

ACL

Le Qiu, Yu-Yin Hsu, and Emmanuele Chersoni. 2026. LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representation. In Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026), pages 203–207, Rabat, Marocco. Association for Computational Linguistics.