SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

Ana-Cristina Rogoz, Gaman Mihaela, Radu Tudor Ionescu


Abstract
In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.
Anthology ID:
2021.acl-short.136
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1073–1079
Language:
URL:
https://aclanthology.org/2021.acl-short.136
DOI:
10.18653/v1/2021.acl-short.136
Bibkey:
Cite (ACL):
Ana-Cristina Rogoz, Gaman Mihaela, and Radu Tudor Ionescu. 2021. SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 1073–1079, Online. Association for Computational Linguistics.
Cite (Informal):
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles (Rogoz et al., ACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2021.acl-short.136.pdf
Code
 MihaelaGaman/SaRoCo
Data
SaRoCo