Abstract
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.
Abstract (translated)
本论文探讨了将符号逻辑知识集成到深层神经网络中,以从嘈杂的群众标签中学习的方法。我们介绍了逻辑引导的从嘈杂群众标签中学习(逻辑-LNCL),这是一种类似于EM迭代逻辑知识蒸馏框架,从嘈杂的标签数据和感兴趣的逻辑规则中学习。与传统EM方法不同,我们的框架包含一个“伪-E步”从逻辑规则中蒸馏出一种新的学习目标,然后用于训练分类器。对两个真实的数据集进行广泛的评估,用于文本情感分类和命名实体识别,表明该框架改进了现有技术,并为从嘈杂的群众标签中学习提供了新的解决方案。
URL
https://arxiv.org/abs/2302.06337
