1 research outputs found
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection
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.
However, in most of the existing approaches, the shared features are completely
assigned to different tasks without selection, which may lead to some useless
and even adverse features integrated into specific tasks. In this paper, we
design a sifted multi-task learning method with a selected sharing layer for
fake news detection. The selected sharing layer adopts gate mechanism and
attention mechanism to filter and select shared feature flows between tasks.
Experiments on two public and widely used competition datasets, i.e. RumourEval
and PHEME, demonstrate that our proposed method achieves the state-of-the-art
performance and boosts the F1-score by more than 0.87%, 1.31%, respectively.Comment: 10 pages, 5 figures, EMNLP 201