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Distant supervision (DS) aims to generate large-scale heuristic labelingcorpus, which is widely used for neural relation extraction currently. However,it heavily suffers from noisy labeling and long-tail distributions problem.Many advanced approaches usually separately address two problems, which ignoretheir mutual interactions. In this paper, we propose a novel framework namedRH-Net, which utilizes Reinforcement learning and Hierarchical relationalsearching module to improve relation extraction. We leverage reinforcementlearning to instruct the model to select high-quality instances. We thenpropose the hierarchical relational searching module to share the semanticsfrom correlative instances between data-rich and data-poor classes. During theiterative process, the two modules keep interacting to alleviate the noisy andlong-tail problem simultaneously. Extensive experiments on widely used NYT dataset clearly show that our method significant improvements over state-of-the-artbaselines.

RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational Searching