Before revision:   In this paper, we propose a novel framework to solve the two problems mentioned above. Firstly, we design a novel reward function to obtain feedback from both correct and noisy data. In addition, we use implicit relations information to improve RL. Secondly, we propose the hierarchical memory extractor (HME), which utilizes the gating mechanism to share the semantics from correlative instances between data-rich and data-poor classes.
After revision:  In this paper, we propose a novel framework named RH-Net, which utilizes Reinforcement learning and Hierarchical relational searching module to improve relation extraction. We leverage reinforcement learning to instruct the model to select high-quality instances. We then propose the hierarchical memory extractor (HME), which utilizes the gating mechanism to share the semantics from correlative instances between data-rich and data-poor classes.