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    <abstract>Metaphors are highly creative constructs of human language that grow old and eventually die. Popular datasets used for metaphor processing tasks were constructed from dated source texts. In this paper, we propose NewsMet, a large high-quality contemporary dataset of news headlines hand-annotated with metaphorical verbs. The dataset comprises headlines from various sources including political, satirical, reliable and fake. Our dataset serves the purpose of evaluation for the tasks of metaphor interpretation and generation. The experiments reveal several insights and limitations of using LLMs to automate metaphor processing tasks as frequently seen in the recent literature. The dataset is publicly available for research purposes https://github.com/AxleBlaze3/NewsMet_Metaphor_Dataset.</abstract>
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%0 Conference Proceedings
%T NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines
%A Joseph, Rohan
%A Liu, Timothy
%A Ng, Aik Beng
%A See, Simon
%A Rai, Sunny
%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 joseph-etal-2023-newsmet
%X Metaphors are highly creative constructs of human language that grow old and eventually die. Popular datasets used for metaphor processing tasks were constructed from dated source texts. In this paper, we propose NewsMet, a large high-quality contemporary dataset of news headlines hand-annotated with metaphorical verbs. The dataset comprises headlines from various sources including political, satirical, reliable and fake. Our dataset serves the purpose of evaluation for the tasks of metaphor interpretation and generation. The experiments reveal several insights and limitations of using LLMs to automate metaphor processing tasks as frequently seen in the recent literature. The dataset is publicly available for research purposes https://github.com/AxleBlaze3/NewsMet_Metaphor_Dataset.
%R 10.18653/v1/2023.findings-acl.641
%U https://aclanthology.org/2023.findings-acl.641
%U https://doi.org/10.18653/v1/2023.findings-acl.641
%P 10090-10104

Markdown (Informal)

[NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines](https://aclanthology.org/2023.findings-acl.641) (Joseph et al., Findings 2023)

• NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines (Joseph et al., Findings 2023)

ACL

• Rohan Joseph, Timothy Liu, Aik Beng Ng, Simon See, and Sunny Rai. 2023. NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10090–10104, Toronto, Canada. Association for Computational Linguistics.