@inproceedings{lee-etal-2019-team,
title = "Team yeon-zi at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data",
author = "Lee, Nayeon and
Liu, Zihan and
Fung, Pascale",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2184",
doi = "10.18653/v1/S19-2184",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data
%A Lee, Nayeon
%A Liu, Zihan
%A Fung, Pascale
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F lee-etal-2019-team
%X 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.
%R 10.18653/v1/S19-2184
%U https://aclanthology.org/S19-2184
%U https://doi.org/10.18653/v1/S19-2184
%P 1052-1056
Markdown (Informal)
[Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data](https://aclanthology.org/S19-2184) (Lee et al., SemEval 2019)
ACL