Learning from Noisy Crowd Labels with Logics
It addresses noisy label problems in crowd-sourced data, offering a new solution for tasks like sentiment analysis and NER, but appears incremental as it builds on EM methods.
The paper tackles learning from noisy crowd labels by integrating symbolic logic into deep neural networks, introducing Logic-LNCL, an EM-like framework that improves state-of-the-art performance on text sentiment classification and named entity recognition datasets.
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.