NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines

Rohan Joseph, Timothy Liu, Aik Beng Ng, Simon See, Sunny Rai


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.
Anthology ID:
2023.findings-acl.641
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10090–10104
Language:
URL:
https://aclanthology.org/2023.findings-acl.641
DOI:
10.18653/v1/2023.findings-acl.641
Bibkey:
Cite (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.
Cite (Informal):
NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines (Joseph et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-acl.641.pdf