Improved Text Classification via Test-Time Augmentation

• URL: http://arxiv.org/abs/2206.13607v1
• Date: Mon, 27 Jun 2022 19:57:27 GMT
• Title: Improved Text Classification via Test-Time Augmentation
• Authors: Helen Lu, Divya Shanmugam, Harini Suresh, John Guttag
• Abstract summary: Test-time augmentation is an established technique to improve the performance of image classification models. We present augmentation policies that yield significant accuracy improvements with language models. Experiments across a binary classification task and dataset show that test-time augmentation can deliver consistent improvements.
• Score: 2.493374942115722
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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