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

Ye Jiang, Johann Petrak, Xingyi Song, Kalina Bontcheva, Diana Maynard


Abstract
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.
Anthology ID:
S19-2146
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
*SEMEVAL
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
840–844
URL:
https://www.aclweb.org/anthology/S19-2146
DOI:
10.18653/v1/S19-2146
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PDF:
https://www.aclweb.org/anthology/S19-2146.pdf