Learning from Noisy Crowd Labels with Logics

TL;DR

This paper presents Logic-LNCL, a novel framework that integrates symbolic logic with neural networks to effectively learn from noisy crowd labels, improving accuracy in text classification tasks.

Contribution

It introduces a unique EM-like iterative framework that distills logic rules into learning targets, enhancing learning from noisy labels.

Findings

Outperforms state-of-the-art methods on sentiment analysis and NER datasets.

Effectively incorporates logic rules to improve label noise robustness.

Demonstrates significant accuracy gains over traditional approaches.

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.