@inproceedings{mao-etal-2024-metapro,
title = "{M}eta{P}ro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling",
author = "Mao, Rui and
He, Kai and
Ong, Claudia and
Liu, Qian and
Cambria, Erik",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.590",
doi = "10.18653/v1/2024.findings-acl.590",
pages = "9891--9908",
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.",
}
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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
%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.
%R 10.18653/v1/2024.findings-acl.590
%U https://aclanthology.org/2024.findings-acl.590
%U https://doi.org/10.18653/v1/2024.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)
ACL