Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification

Zhuoran Liu, Shivali Goel, Mukund Yelahanka Raghuprasad, Smaranda Muresan


Abstract
The paper presents Columbia team’s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous work suffered from data sparsity, which potentially limited the application of more sophisticated neural architecture to this task. We mitigate this problem by proposing a multi-task learning approach together with language model fine-tuning. Our attention-based model allows different tasks to leverage different level of information. Our system ranked 6th overall with an F1-score of 36.25 on stance classification and F1 of 22.44 on rumour verification.
Anthology ID:
S19-2194
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:
1110–1114
Language:
URL:
https://aclanthology.org/S19-2194
DOI:
10.18653/v1/S19-2194
Bibkey:
Cite (ACL):
Zhuoran Liu, Shivali Goel, Mukund Yelahanka Raghuprasad, and Smaranda Muresan. 2019. Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1110–1114, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification (Liu et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2194.pdf