  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.
