Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages

Ehsan Aghazadeh, Mohsen Fayyaz, Yadollah Yaghoobzadeh


Abstract
Human languages are full of metaphorical expressions. Metaphors help people understand the world by connecting new concepts and domains to more familiar ones. Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems. In this paper, we investigate this hypothesis for PLMs, by probing metaphoricity information in their encodings, and by measuring the cross-lingual and cross-dataset generalization of this information. We present studies in multiple metaphor detection datasets and in four languages (i.e., English, Spanish, Russian, and Farsi). Our extensive experiments suggest that contextual representations in PLMs do encode metaphorical knowledge, and mostly in their middle layers. The knowledge is transferable between languages and datasets, especially when the annotation is consistent across training and testing sets. Our findings give helpful insights for both cognitive and NLP scientists.
Anthology ID:
2022.acl-long.144
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2037–2050
Language:
URL:
https://aclanthology.org/2022.acl-long.144
DOI:
10.18653/v1/2022.acl-long.144
Bibkey:
Cite (ACL):
Ehsan Aghazadeh, Mohsen Fayyaz, and Yadollah Yaghoobzadeh. 2022. Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2037–2050, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (Aghazadeh et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.144.pdf
Software:
 2022.acl-long.144.software.zip
Code
 ehsanaghazadeh/metaphors_in_plms