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 Generic
%1 conf/semeval/BarisSS19
%A Baris, Ipek
%A Schmelzeisen, Lukas
%A Staab, Steffen
%B Proceedings of the 13th International Workshop on Semantic Evaluation
%C Minneapolis, Minnesota, USA
%D 2019
%I Association for Computational Linguistics
%K 2019-06 ibaris lschmelzeisen myown staab west.uni-koblenz.de
%P 1105--1109
%T CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
%U https://aclweb.org/anthology/papers/S/S19/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.
@conference{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 = {2019-05-06T13:58:16.000+0200},
address = {Minneapolis, Minnesota, USA},
author = {Baris, Ipek and Schmelzeisen, Lukas and Staab, Steffen},
biburl = {https://www.bibsonomy.org/bibtex/23305713de21251d1c0d02754568a8285/ibaris},
booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
interhash = {8ecf60543c716db98189eb98c772be4f},
intrahash = {3305713de21251d1c0d02754568a8285},
keywords = {2019-06 ibaris lschmelzeisen myown staab west.uni-koblenz.de},
language = {english},
month = jun,
pages = {1105--1109},
publisher = {Association for Computational Linguistics},
timestamp = {2019-07-02T17:59:03.000+0200},
title = {CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors},
url = {https://aclweb.org/anthology/papers/S/S19/S19-2193/},
venue = {*SEMEVAL},
year = 2019
}