Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network

By Ye Jiang, Johann Petrak, Xingyi Song, Kalina Bontcheva, Diana Maynard. 2019

50 citations recorded by the source corpus.

This paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system 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.

Subfields: Artificial Intelligence

Citations in the corpus

• Big Bird: Transformers for Longer Sequences (2020, neutral): “[41] Y.”