UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs

Chiyu Zhang, Arun Rajendran, Muhammad Abdul-Mageed


Abstract
We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3% accuracy) on the ‘labels-by-publisher’ sub-task and top 24 out of 44 teams (68.3% accuracy) on the ‘labels-by-article’ sub-task (65.3% accuracy). We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the ‘labels-by-article’ sub-task.
Anthology ID:
S19-2188
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1072–1077
Language:
URL:
https://aclanthology.org/S19-2188
DOI:
10.18653/v1/S19-2188
Bibkey:
Cite (ACL):
Chiyu Zhang, Arun Rajendran, and Muhammad Abdul-Mageed. 2019. UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1072–1077, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs (Zhang et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2188.pdf