State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier. We propose to discover those feature-value combinations (i.e., patterns) that strongly correlate with correct resp. erroneous predictions to obtain a global and interpretable description for arbitrary classifiers. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover a good pattern set, we develop the efficient Premise algorithm. Through an extensive set of experiments we show it performs very well in practice on both synthetic and real-world data. Unlike existing solutions, it ably recovers ground truth patterns, even on highly imbalanced data over many features. Through two case studies on Visual Question Answering and Named Entity Recognition, we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers.

翻译：最先进的深层次学习方法在很多任务上取得了人性化的成绩,但还是犯了错误。用容易解释的术语来描述这些错误,可以洞察到分类者是否容易作出系统错误,但也可以采取行动和改进分类者。我们提议发现与正确重写密切相关的特征价值组合(即模式),错误预测,以获得任意分类者的全球和可解释描述。我们显示了一个更笼统的标签描述问题的例子,我们用最低描述长度原则来说明。为了发现一个良好的模式集,我们开发了高效的预感算法。通过一系列广泛的实验,我们展示了它在合成数据和实际数据两方面都非常出色地实际表现。与现有的解决办法不同,它可以恢复地面真相模式,甚至恢复在许多特征高度不平衡的数据。我们通过对视觉问题回答和名称实体识别的两个案例研究,确认Premize对现代NLP分类者所作的系统错误进行了明确和可操作的洞察。