  A recent study by Feldman (2020) proposed a long-tail theory to explain the
memorization behavior of deep learning models. However, memorization has not
been empirically verified in the context of NLP, a gap addressed by this work.
In this paper, we use three different NLP tasks to check if the long-tail
theory holds. Our experiments demonstrate that top-ranked memorized training
instances are likely atypical, and removing the top-memorized training
instances leads to a more serious drop in test accuracy compared with removing
training instances randomly. Furthermore, we develop an attribution method to
better understand why a training instance is memorized. We empirically show
that our memorization attribution method is faithful, and share our interesting
finding that the top-memorized parts of a training instance tend to be features
negatively correlated with the class label.
