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.
%0 Conference Paper
%1 conf/semeval/BarisSS19
%A Baris, Ipek
%A Schmelzeisen, Lukas
%A Staab, Steffen
%B SemEval@NAACL-HLT
%D 2019
%E May, Jonathan
%E Shutova, Ekaterina
%E Herbelot, Aurélie
%E Zhu, Xiaodan
%E Apidianaki, Marianna
%E Mohammad, Saif M.
%I Association for Computational Linguistics
%K @AnalyticComp AnalyticComputing dblp myown
%P 1105-1109
%R 10.18653/v1/s19-2193
%T CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.
%U https://www.aclweb.org/anthology/S19-2193/
%X 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.
%@ 978-1-950737-06-2
@inproceedings{conf/semeval/BarisSS19,
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.},
added-at = {2020-11-26T10:00:37.000+0100},
author = {Baris, Ipek and Schmelzeisen, Lukas and Staab, Steffen},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/242f0d00808b61a6cc0a7d69aa701b464/lschmelzeisen},
booktitle = {SemEval@NAACL-HLT},
crossref = {conf/semeval/2019},
doi = {10.18653/v1/s19-2193},
editor = {May, Jonathan and Shutova, Ekaterina and Herbelot, Aurélie and Zhu, Xiaodan and Apidianaki, Marianna and Mohammad, Saif M.},
ee = {https://www.aclweb.org/anthology/S19-2193/},
interhash = {8ecf60543c716db98189eb98c772be4f},
intrahash = {42f0d00808b61a6cc0a7d69aa701b464},
isbn = {978-1-950737-06-2},
keywords = {@AnalyticComp AnalyticComputing dblp myown},
language = {English},
month = jun,
pages = {1105-1109},
publisher = {Association for Computational Linguistics},
timestamp = {2020-11-26T09:24:59.000+0100},
title = {CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.},
url = {https://www.aclweb.org/anthology/S19-2193/},
year = 2019
}
