LANE: Label-Aware Noise Elimination for Fine-Grained Text Classification

In this paper, we propose Label-Aware Noise Elimination (LANE), a new approach to learning with noisy labels. At its core, LANE introduces a new metric---label-aware margin---aimed at quantifying the degree of noise of each training example (or quality thereof). LANE leverages the semantic relations between classes and monitors the training dynamics of the model on each training example to dynamically lower the weight of training examples that are perceived to have noisy labels. We test the effectiveness of LANE on multiple text classification tasks and benchmark our approach on a wide variety of datasets with various numbers of classes and amounts of label noise. LANE considerably outperforms strong baselines on all datasets and settings, obtaining significant improvements ranging from an average improvement of 2.88% in F1 on manually annotated datasets to a considerable average improvement of 4.75% F1 on datasets with high level of injected label noise. We carry out a comprehensive analysis of LANE and identify the key components that lead to its success.