Abstract
We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors’ propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.- Anthology ID:
- D18-1140
- 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:
- 1115–1121
- Language:
- URL:
- https://aclanthology.org/D18-1140
- DOI:
- 10.18653/v1/D18-1140
- Cite (ACL):
- Y. Alex Kolchinski and Christopher Potts. 2018. Representing Social Media Users for Sarcasm Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1115–1121, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Representing Social Media Users for Sarcasm Detection (Kolchinski & Potts, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/D18-1140.pdf
- Code
- kolchinski/reddit-sarc
- Data
- SARC