Adversarial Training for Satire Detection: Controlling for Confounding Variables

Robert McHardy, Heike Adel, Roman Klinger


Abstract
The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesize that this encourages the models to learn characteristics for different publication sources (e.g., “The Onion” vs. “The Guardian”) rather than characteristics of satire, leading to poor generalization performance to unseen publication sources. We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source. On a large novel data set collected from German news (which we make available to the research community), we observe comparable satire classification performance and, as desired, a considerable drop in publication classification performance with adversarial training. Our analysis shows that the adversarial component is crucial for the model to learn to pay attention to linguistic properties of satire.
Anthology ID:
N19-1069
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
660–665
Language:
URL:
https://aclanthology.org/N19-1069
DOI:
10.18653/v1/N19-1069
Bibkey:
Cite (ACL):
Robert McHardy, Heike Adel, and Roman Klinger. 2019. Adversarial Training for Satire Detection: Controlling for Confounding Variables. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 660–665, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Adversarial Training for Satire Detection: Controlling for Confounding Variables (McHardy et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1069.pdf
Supplementary:
 N19-1069.Supplementary.pdf
Presentation:
 N19-1069.Presentation.pdf
Video:
 https://vimeo.com/355743083