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：本文探索将符号逻辑知识融入深度神经网络，以应对嘈杂众包标注的学习问题。我们提出了逻辑引导的嘈杂众包标注学习框架（Logic-LNCL），这是一种类似期望最大化（EM）的迭代逻辑知识蒸馏框架，既能从带噪标注数据中学习，也能从相关逻辑规则中学习。与传统EM方法不同，我们的框架包含一个“伪E步”，该步骤从逻辑规则中提炼出一种新型学习目标，随后在“伪M步”中用于训练分类器。在文本情感分类与命名实体识别两个真实数据集上的广泛评估表明，所提出的框架改进了现有最优方法，为从嘈杂众包标注中学习提供了新的解决方案。