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    <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.</abstract>
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%0 Conference Proceedings
%T MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling
%A Mao, Rui
%A He, Kai
%A Ong, Claudia
%A Liu, Qian
%A Cambria, Erik
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F mao-etal-2024-metapro
%X 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.
%U https://aclanthology.org/2024.findings-acl.590
%P 9891-9908

Markdown (Informal)

[MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling](https://aclanthology.org/2024.findings-acl.590) (Mao et al., Findings 2024)

• MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (Mao et al., Findings 2024)

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 and virtual meeting. Association for Computational Linguistics.