Abstract
We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.- Anthology ID:
- D18-1060
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 607–613
- Language:
- URL:
- https://aclanthology.org/D18-1060
- DOI:
- 10.18653/v1/D18-1060
- Cite (ACL):
- Ge Gao, Eunsol Choi, Yejin Choi, and Luke Zettlemoyer. 2018. Neural Metaphor Detection in Context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 607–613, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Neural Metaphor Detection in Context (Gao et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1060.pdf
- Code
- gao-g/metaphor-in-context