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        <title>Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation</title>
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        <namePart type="given">Aina</namePart>
        <namePart type="family">Garí Soler</namePart>
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    <name type="personal">
        <namePart type="given">Matthieu</namePart>
        <namePart type="family">Labeau</namePart>
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    <name type="personal">
        <namePart type="given">Chloé</namePart>
        <namePart type="family">Clavel</namePart>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate data collection and speed up the WMN annotation process, we introduce the task of detecting WMN indicators – utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data.</abstract>
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%0 Conference Proceedings
%T Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation
%A Garí Soler, Aina
%A Labeau, Matthieu
%A Clavel, Chloé
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F gari-soler-etal-2025-toward
%X Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate data collection and speed up the WMN annotation process, we introduce the task of detecting WMN indicators – utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data.
%U https://aclanthology.org/2025.findings-emnlp.1337/
%P 24580-24596

Markdown (Informal)

[Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation](https://aclanthology.org/2025.findings-emnlp.1337/) (Garí Soler et al., Findings 2025)

• Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation (Garí Soler et al., Findings 2025)

ACL

• Aina Garí Soler, Matthieu Labeau, and Chloé Clavel. 2025. Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 24580–24596, Suzhou, China. Association for Computational Linguistics.