Abstract
This paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system1 uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.
Cite
CITATION STYLE
Jiang, Y., Petrak, J., Song, X., Bontcheva, K., & Maynard, D. (2019). Team Bertha von Suttner at SemEval-2019 task 4: Hyperpartisan news detection using ELMo sentence representation convolutional network. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 840–844). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2146
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