@inproceedings{baris-etal-2019-clearumor,
    title = "{CLEAR}umor at {S}em{E}val-2019 Task 7: {C}onvo{L}ving {ELM}o Against Rumors",
    author = "Baris, Ipek  and
      Schmelzeisen, Lukas  and
      Staab, Steffen",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2193/",
    doi = "10.18653/v1/S19-2193",
    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."
}