Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News

Daniel Shaprin, Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov


Abstract
We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extreme-right. We proposed an approach based on BERT with fine-tuning, which was ranked 7th out 28 teams on the distantly supervised dataset, where all articles from a hyperpartisan/non-hyperpartisan news outlet are considered to be hyperpartisan/non-hyperpartisan. On a manually annotated test dataset, where human annotators double-checked the labels, we were ranked 29th out of 42 teams.
Anthology ID:
S19-2176
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1012–1015
Language:
URL:
https://aclanthology.org/S19-2176
DOI:
10.18653/v1/S19-2176
Bibkey:
Cite (ACL):
Daniel Shaprin, Giovanni Da San Martino, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1012–1015, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News (Shaprin et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2176.pdf