Institute for Web Science and Technologies · Universität Koblenz - Landau

CLEARumor: ConvoLving ELMo Against Rumors

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Ipek Baris

In this talk, I will present our submission, namely CLEARumor, for RumourEval 2019 [1] and I aim to get feedback from you for improving CLEARumor. RumourEval consists of two tasks: stance detection towards a rumour and identifying veracity of a rumour. The goal of stance detection is to label the type of interaction between a rumourous tweet and a reply tweet, as support, query, deny or comment. The other task is to predict the veracity of a given rumour as true, false or unverified. For stance detection, CLEARumor uses CNN based neural network using pre-trained ELMo embeddings [2] with auxiliary features which is extracted from metadata of post. For veracity detection, it leverages probabilistic estimations from the first task with further auxiliary features. Our submission was ranked 2nd on veracity task.

[1] Gorrell, Genevieve, et al. "RumourEval 2019: Determining Rumour Veracity and Support for Rumours." arXiv preprint arXiv:1809.06683 (2018).
[2] Peters, Matthew E., et al. "Deep contextualized word representations." arXiv preprint arXiv:1802.05365 (2018).

28.03.19 - 10:15
B 016