Learning from Noisy Crowd Labels with Logics

作     者：Chen, Zhijun Sun, Hailong He, Haoqian Chen, Pengpeng 

作者机构：SKLSDE Lab Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China China’s Aviation System Engineering Research Institute Beijing China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Crowdsourcing 

摘      要：This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a pseudo-E-step that distills from the logic rules a new type of learning target, which is then used in the pseudo-M-step for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels. Copyright © 2023, The Authors. All rights reserved.