Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data

Nayeon Lee, Zihan Liu, Pascale Fung


Abstract
This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the noise inherent in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8% accuracy in the final by-article dataset without ensemble learning.
Anthology ID:
S19-2184
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:
1052–1056
Language:
URL:
https://aclanthology.org/S19-2184
DOI:
10.18653/v1/S19-2184
Bibkey:
Cite (ACL):
Nayeon Lee, Zihan Liu, and Pascale Fung. 2019. Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1052–1056, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data (Lee et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2184.pdf
Code
 zliucr/hyperpartisan-news-detection