no code implementations • ACL 2021 • Lianwei Wu, Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi
From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i. e., the overall responses) among all users and adjust the proportion between them to generate global evidence.
no code implementations • ICCV 2021 • Yuan Rao, Jiangqun Ni
With wide applications of image editing tools, forged images (splicing, copy-move, removal and etc.)
no code implementations • ACL 2020 • Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir
Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims.
no code implementations • 21 Apr 2020 • Lianwei Wu, Yuan Rao
In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection.
no code implementations • 16 Sep 2019 • Lianwei Wu, Yuan Rao, Ambreen Nazir, Haolin Jin
A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet.
no code implementations • IJCNLP 2019 • Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun
Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks.