论文标题
通过测试时间增加改进文本分类
Improved Text Classification via Test-Time Augmentation
论文作者
论文摘要
测试时间增强 - 跨测试输入示例的预测的聚合 - 是一种改善图像分类模型性能的既定技术。重要的是，TTA可用于改善事后模型性能，而无需其他培训。尽管可以将测试时间增强（TTA）应用于任何数据模式，但它在NLP中的采用有限，部分原因是难以识别标签保护转换。在本文中，我们提出了增强政策，通过语言模型可以改善准确的准确性。一个关键发现是，增强政策设计（例如，单个非确定性增强产生的样本数量）对TTA的好处有很大的影响。二进制分类任务和数据集进行的实验表明，测试时间增加可以对当前最新方法进行一致的改进。
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.
