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
- 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)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S19-2188.pdf