Deep reinforcement learning based ensemble model for rumor tracking
作者:
Highlights:
• By exploring plenty of basic models, we proposed an aggregated model named RL-ERT to solve the rumor tracking task.
• We propose a reinforcement learning based bagging algorithm to aggregate basic models into a macrocosm.
• We conduct experiments on public benchmark datasets, and the experimental results show the rationality and superiority of RL-ERT.
摘要
•By exploring plenty of basic models, we proposed an aggregated model named RL-ERT to solve the rumor tracking task.•We propose a reinforcement learning based bagging algorithm to aggregate basic models into a macrocosm.•We conduct experiments on public benchmark datasets, and the experimental results show the rationality and superiority of RL-ERT.
论文关键词:Rumor tracking,Natural language processing,Deep learning,Reinforcement learning
论文评审过程:Received 15 September 2020, Revised 23 December 2020, Accepted 14 March 2021, Available online 26 March 2021, Version of Record 27 September 2021.
论文官网地址:https://doi.org/10.1016/j.is.2021.101772