Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT

SEMEVAL 2019 · Osman Mutlu, Ozan Arkan Can, Erenay Dayanik ·

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 system ranked 8th out of 42 teams with 78.3{\%} accuracy on the held-out test dataset.

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Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay • WordPiece

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Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT

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