Abstract
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only achieves better or comparable performance to the state-of-the-art, but also is more explainable and interpretable compared to existing models, attributing to its ability of accounting for external knowledge of FrameNet.
Cite
CITATION STYLE
Li, Y., Wang, S., Lin, C., Guerin, F., & Barrault, L. (2023). FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 1550–1555). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.eacl-main.114
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