Abstract
Distant supervision (DS) has the advantage of automatically annotate large amounts of data and has been widely used for relation classification. Despite its efficiency, it often suffers from the label noise problem, which would impair the performance of relation classification. Recently, there are two ways to solve the label noise problem. The first way is to use multi-instance learning to consider the noises of instances, but they do not perform well for sentence-level prediction. The second way is to use reinforcement learning or adversarial learning to directly find noisy label instances but with high computational overhead and poor performance. In this paper, we propose to use the natural language inference (NLI) model to evaluate the quality of the instances directly, and select the high-quality instances as refined training data for sentence-level relation classification. Due to the lack of high-quality supervised data, we use reinforcement learning to train the NLI model. Experimental results on two human re-annotated NYT datasets show the effectiveness and efficiency of our method at the sentence-level relation classification. The source code of this paper can be found in https://github.com/xubodhu/RLRC.
This paper was supported by the National Natural Science Foundation of China (61906035), Shanghai Sailing Program (19YF1402300) and National Natural Science Foundation of China (61972081).
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Xu, B., Zhao, X., Sha, C., Zhang, M., Song, H. (2021). Reinforced Natural Language Inference for Distantly Supervised Relation Classification. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_29
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