Abstract
Metaphor interpretation is a difficult task in natural language understanding. The development of relevant techniques in this domain is slow, mostly because of the lack of large annotated datasets and effective pre-trained language models (PLMs) for metaphor learning. Thus, we propose a large annotated dataset and a PLM for the metaphor interpretation task. Our foundation model is based on a novel anomalous language modeling (ALM) method, which we benchmark with comparable PLM baselines on the new dataset, finding that it largely improves model performance on metaphor identification and interpretation.- Anthology ID:
- 2024.findings-acl.590
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9891–9908
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.590
- DOI:
- 10.18653/v1/2024.findings-acl.590
- Cite (ACL):
- Rui Mao, Kai He, Claudia Ong, Qian Liu, and Erik Cambria. 2024. MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9891–9908, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (Mao et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.590.pdf