@InProceedings{lee-liu-fung:2019:S19-2,
  author    = {Lee, Nayeon  and  Liu, Zihan  and  Fung, Pascale},
  title     = {Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1052--1056},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-2184}
}