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)
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.semeval-1.246.pdf