Abstract
Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0% F1-score on the Twitter dataset and 66.3% F1-score on Reddit dataset.- Anthology ID:
- 2020.figlang-1.8
- Volume:
- Proceedings of the Second Workshop on Figurative Language Processing
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 61–66
- Language:
- URL:
- https://aclanthology.org/2020.figlang-1.8
- DOI:
- 10.18653/v1/2020.figlang-1.8
- Cite (ACL):
- Amit Kumar Jena, Aman Sinha, and Rohit Agarwal. 2020. C-Net: Contextual Network for Sarcasm Detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 61–66, Online. Association for Computational Linguistics.
- Cite (Informal):
- C-Net: Contextual Network for Sarcasm Detection (Kumar Jena et al., Fig-Lang 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.figlang-1.8.pdf