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        <title>Multilingual Multi-Figurative Language Detection</title>
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        <namePart type="given">Huiyuan</namePart>
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    <name type="personal">
        <namePart type="given">Malvina</namePart>
        <namePart type="family">Nissim</namePart>
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    <abstract>Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it’s highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.589</identifier>
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%0 Conference Proceedings
%T Multilingual Multi-Figurative Language Detection
%A Lai, Huiyuan
%A Toral, Antonio
%A Nissim, Malvina
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lai-etal-2023-multilingual
%X Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it’s highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.
%R 10.18653/v1/2023.findings-acl.589
%U https://aclanthology.org/2023.findings-acl.589
%U https://doi.org/10.18653/v1/2023.findings-acl.589
%P 9254-9267

Markdown (Informal)

[Multilingual Multi-Figurative Language Detection](https://aclanthology.org/2023.findings-acl.589) (Lai et al., Findings 2023)

• Multilingual Multi-Figurative Language Detection (Lai et al., Findings 2023)

ACL

• Huiyuan Lai, Antonio Toral, and Malvina Nissim. 2023. Multilingual Multi-Figurative Language Detection. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9254–9267, Toronto, Canada. Association for Computational Linguistics.