论文标题
比较特征的重要性和规则提取，以解释性的文本数据可解释性
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
论文作者
论文摘要
复杂的机器学习算法越来越多地用于涉及文本数据的关键任务中，从而开发了可解释性方法。在本地方法中，已经出现了两个家庭：那些计算每个功能的重要性得分以及那些提取简单逻辑规则的那些分数。在本文中，我们表明，即使使用不同的方法，使用不同的方法可能会导致出乎意料的不同解释，即使我们将其期望为定性巧合的简单模型。为了量化这种效果，我们提出了一种新方法来比较不同方法产生的解释。
Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods.
