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Improved Text Classification via Test-Time Augmentation. (arXiv:2206.13607v1 [cs.LG])
cs.CL updates on arXiv.org arxiv.org
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 …
arxiv augmentation classification lg test text text classification time