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        <title>A framework for annotating and modelling intentions behind metaphor use</title>
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    <abstract>Metaphors are part of everyday language and shape the way in which we conceptualize the world. Moreover, they play a multifaceted role in communication, making their understanding and generation a challenging task for language models (LMs). While there has been extensive work in the literature linking metaphor to the fulfilment of individual intentions, no comprehensive taxonomy of such intentions, suitable for natural language processing (NLP) applications, is available to present day. In this paper, we propose a novel taxonomy of intentions commonly attributed to metaphor, which comprises 9 categories. We also release the first dataset annotated for intentions behind metaphor use. Finally, we use this dataset to test the capability of large language models (LLMs) in inferring the intentions behind metaphor use, in zero- and in-context few-shot settings. Our experiments show that this is still a challenge for LLMs.</abstract>
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%0 Conference Proceedings
%T A framework for annotating and modelling intentions behind metaphor use
%A Michelli, Gianluca
%A Tong, Xiaoyu
%A Shutova, Ekaterina
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F michelli-etal-2026-framework
%X Metaphors are part of everyday language and shape the way in which we conceptualize the world. Moreover, they play a multifaceted role in communication, making their understanding and generation a challenging task for language models (LMs). While there has been extensive work in the literature linking metaphor to the fulfilment of individual intentions, no comprehensive taxonomy of such intentions, suitable for natural language processing (NLP) applications, is available to present day. In this paper, we propose a novel taxonomy of intentions commonly attributed to metaphor, which comprises 9 categories. We also release the first dataset annotated for intentions behind metaphor use. Finally, we use this dataset to test the capability of large language models (LLMs) in inferring the intentions behind metaphor use, in zero- and in-context few-shot settings. Our experiments show that this is still a challenge for LLMs.
%U https://aclanthology.org/2026.starsem-conference.4/
%P 63-82

Markdown (Informal)

[A framework for annotating and modelling intentions behind metaphor use](https://aclanthology.org/2026.starsem-conference.4/) (Michelli et al., *SEM 2026)

• A framework for annotating and modelling intentions behind metaphor use (Michelli et al., *SEM 2026)

ACL

• Gianluca Michelli, Xiaoyu Tong, and Ekaterina Shutova. 2026. A framework for annotating and modelling intentions behind metaphor use. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 63–82, San Diego, California, United States. Association for Computational Linguistics.