C-Net: Contextual Network for Sarcasm Detection

Amit Kumar Jena, Aman Sinha, Rohit Agarwal


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
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–66
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.8
DOI:
10.18653/v1/2020.figlang-1.8
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.8.pdf
Video:
 http://slideslive.com/38929698