@inproceedings{li-etal-2019-rumor-detection,
title = "Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning",
author = "Li, Quanzhi and
Zhang, Qiong and
Si, Luo",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1113",
doi = "10.18653/v1/P19-1113",
pages = "1173--1179",
abstract = "In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches.",
}
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%0 Conference Proceedings
%T Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning
%A Li, Quanzhi
%A Zhang, Qiong
%A Si, Luo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-rumor-detection
%X In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches.
%R 10.18653/v1/P19-1113
%U https://aclanthology.org/P19-1113
%U https://doi.org/10.18653/v1/P19-1113
%P 1173-1179
Markdown (Informal)
[Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning](https://aclanthology.org/P19-1113) (Li et al., ACL 2019)
ACL