Robust Text Classifier on Test-Time Budgets

We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.

我们设计了一个通用框架，用于学习一个强大的文本分类模型，该模型在测试时间时在不同的选择预算（又称选择率）下实现高精度。我们采用与现有方法不同的方法，并通过低复杂性选择器学习动态过滤大量不重要的单词，以使任何高复杂性的最先进的分类器都只需要处理一小部分文本，与该文本有关目标任务。为此，我们提出了一种数据聚合方法来训练分类器，从而使其能够在破裂的句子上实现竞争性能。在四个基准的文本分类任务上，我们证明该框架在各种选择预算中的准确性几乎没有降解，从而获得了一致的加速。