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.
翻译:本文探讨了将象征性逻辑知识纳入深层神经网络以从吵闹的人群标签中学习的问题。 我们引入了从Noisy Crowd Labels(Logic-LNCL)中学习的逻辑指南,这是一个类似于EM的迭代逻辑知识蒸馏框架,既从吵闹的标签数据和逻辑规则中学习。 与传统的EM方法不同,我们的框架包含一个“ pseudo-E-step ”,它从逻辑规则中提炼出一种新的学习目标,然后在“pseudo-M-step”中用于培训分类师。 对两个真实世界的文本情绪分类和名称实体识别数据集的广泛评估表明,拟议的框架改进了最新技术,并为从吵闹的人群标签中学习提供了新的解决方案。