@inproceedings{kumar-jena-etal-2020-c,
title = "{C}-Net: Contextual Network for Sarcasm Detection",
author = "Kumar Jena, Amit and
Sinha, Aman and
Agarwal, Rohit",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.8",
doi = "10.18653/v1/2020.figlang-1.8",
pages = "61--66",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T C-Net: Contextual Network for Sarcasm Detection
%A Kumar Jena, Amit
%A Sinha, Aman
%A Agarwal, Rohit
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F kumar-jena-etal-2020-c
%X 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.
%R 10.18653/v1/2020.figlang-1.8
%U https://aclanthology.org/2020.figlang-1.8
%U https://doi.org/10.18653/v1/2020.figlang-1.8
%P 61-66
Markdown (Informal)
[C-Net: Contextual Network for Sarcasm Detection](https://aclanthology.org/2020.figlang-1.8) (Kumar Jena et al., Fig-Lang 2020)
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.