Learning from Noisy Crowd Labels with Logics

• URL: http://arxiv.org/abs/2302.06337v3
• Date: Sun, 19 Mar 2023 13:01:47 GMT
• Title: Learning from Noisy Crowd Labels with Logics
• Authors: Zhijun Chen, Hailong Sun, Haoqian He, Pengpeng Chen
• Abstract summary: We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework. We show that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels.
• Score: 10.574859201380612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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