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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2184.pdf
- Code
- zliucr/hyperpartisan-news-detection