Soft Label Memorization-Generalization for Natural Language Inference
This work addresses the problem of handling ambiguous human annotations for NLI tasks, offering an incremental fine-tuning approach to enhance model generalization.
The paper tackles the challenge of using soft labels from multiple annotators to improve generalization in deep neural networks for Natural Language Inference, achieving improved performance by injecting only 0.03% of soft label training data.
Often when multiple labels are obtained for a training example it is assumed that there is an element of noise that must be accounted for. It has been shown that this disagreement can be considered signal instead of noise. In this work we investigate using soft labels for training data to improve generalization in machine learning models. However, using soft labels for training Deep Neural Networks (DNNs) is not practical due to the costs involved in obtaining multiple labels for large data sets. We propose soft label memorization-generalization (SLMG), a fine-tuning approach to using soft labels for training DNNs. We assume that differences in labels provided by human annotators represent ambiguity about the true label instead of noise. Experiments with SLMG demonstrate improved generalization performance on the Natural Language Inference (NLI) task. Our experiments show that by injecting a small percentage of soft label training data (0.03% of training set size) we can improve generalization performance over several baselines.