WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation

Guillaume Daval-Frerot, Yannick Weis


Abstract
In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset.
Anthology ID:
2020.semeval-1.246
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1865–1874
Language:
URL:
https://aclanthology.org/2020.semeval-1.246
DOI:
10.18653/v1/2020.semeval-1.246
Bibkey:
Cite (ACL):
Guillaume Daval-Frerot and Yannick Weis. 2020. WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1865–1874, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation (Daval-Frerot & Weis, SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.246.pdf