Abstract
This paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019). We use pretrained BERT (Devlin et al., 2018) architecture and investigate the effect of different fine tuning regimes on the final classification task. We show that additional pretraining on news domain improves the performance on the Hyperpartisan News Detection task. Our system1 ranked 8th out of 42 teams with 78.3% accuracy on the held-out test dataset.
Cite
CITATION STYLE
Mutlu, O., Can, O. A., & Dayanık, E. (2019). Team Howard Beale at SemEval-2019 task 4: Hyperpartisan news detection with BERT. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 1007–1011). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2175
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