Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

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.

Keywords

Natural language processing

Discipline

Computer Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 2022 May 22 - 27

First Page

6265

Last Page

6278

Identifier

10.18653/v1/2022.acl-long.434

City or Country

Dublin, Ireland

Additional URL

https://doi.org/10.18653/v1/2022.acl-long.434

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