FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
Yucheng Li, Shun Wang, Chenghua Lin, Frank Guerin, Loic Barrault
Abstract
In this paper, we propose FrameBERT, a BERT-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.- Anthology ID:
- 2023.eacl-main.114
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1558–1563
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.114
- DOI:
- 10.18653/v1/2023.eacl-main.114
- Cite (ACL):
- Yucheng Li, Shun Wang, Chenghua Lin, Frank Guerin, and Loic Barrault. 2023. FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1558–1563, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (Li et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.eacl-main.114.pdf