@InProceedings{baris-schmelzeisen-staab:2019:S19-2,
  author    = {Baris, Ipek  and  Schmelzeisen, Lukas  and  Staab, Steffen},
  title     = {CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1105--1109},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-2193}
}