Abstract
This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.- Anthology ID:
- S19-2193
- 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:
- 1105–1109
- Language:
- URL:
- https://aclanthology.org/S19-2193
- DOI:
- 10.18653/v1/S19-2193
- Cite (ACL):
- Ipek Baris, Lukas Schmelzeisen, and Steffen Staab. 2019. CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1105–1109, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors (Baris et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/S19-2193.pdf
- Code
- lschmelzeisen/clearumor