# Improved Text Classification via Test-Time Augmentation

**Authors:** Helen Lu, Divya Shanmugam, Harini Suresh, John Guttag  
**arXiv:** [2206.13607](https://arxiv.org/abs/2206.13607)  
**Date:** 2022-06-27  
**Summary page:** https://www.summarizepaper.com/fr/paper/2206.13607v1/

## Abstract

Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classification models. Importantly, TTA can be used to improve model performance post-hoc, without additional training. Although test-time augmentation (TTA) can be applied to any data modality, it has seen limited adoption in NLP due in part to the difficulty of identifying label-preserving transformations. In this paper, we present augmentation policies that yield significant accuracy improvements with language models. A key finding is that augmentation policy design -- for instance, the number of samples generated from a single, non-deterministic augmentation -- has a considerable impact on the benefit of TTA. Experiments across a binary classification task and dataset show that test-time augmentation can deliver consistent improvements over current state-of-the-art approaches.