论文标题
查找：人类在深层文本分类器中调试
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
论文作者
论文摘要
自从获得完美的培训数据集（即，几乎不可能具有相当大的，公正且代表性的数据集）以来，几乎不可能），许多现实世界中的文本分类器对可用但不完美的数据集进行了培训。因此，这些分类器可能具有不良属性。例如，它们可能会对某些子人群有偏见，或者由于过度拟合而在野外无法有效地工作。在本文中，我们提出了找到 - 一个框架，该框架可以通过禁用无关的隐藏特征来调试深度学习文本分类器。实验表明，通过使用查找，人类可以改善CNN文本分类器，这些分类器是在不同类型的不完美数据集中训练的（包括具有偏见的数据集和具有不同火车测试分布的数据集）。
Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND -- a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).
