Mao, RuiHe, KaiOng, ClaudiaLiu, QianCambria, Erik2024-11-042024-11-042024(2024). Findings of the Association for Computational Linguistics ACL 2024, 9891-9908.https://hdl.handle.net/2292/70392Metaphor 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.Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htmhttps://creativecommons.org/licenses/by/4.0/Conference Item10.18653/v1/2024.findings-acl.5902024-10-19Copyright: ACLhttp://purl.org/eprint/accessRights/OpenAccess