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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/S19-2176.pdf