A Three-Stage Curriculum Learning Framework with Hierarchical Label Smoothing for Fine-Grained Entity Typing

In this paper, we study the noisy labeling problem on the fine-grained entity typing (FET) task. Most existing methods propose to divide the training data into "clean" and "noisy" sets and use different strategies to deal with them during the training process. However, the "clean" samples used in these methods are not actually clean, some of them also contain noisy labels. To overcome this issue, we propose a three-stage curriculum learning framework with hierarchical label smoothing to train the FET model, which can use relatively clean data to train the model and prevent the model from overfitting to noisy labels. Experiments conducted on three widely used FET datasets show that our method achieves the new state-of-the-art performance. Our code is publicly available at https://github.com/xubodhu/NFETC-CLHLS.