Post-hoc explanation methods are an important tool for increasing model
transparency for users. Unfortunately, the currently used methods for
attributing token importance often yield diverging patterns. In this work, we
study potential sources of disagreement across methods from a linguistic
perspective. We find that different methods systematically select different
classes of words and that methods that agree most with other methods and with
humans display similar linguistic preferences. Token-level differences between
methods are smoothed out if we compare them on the syntactic span level. We
also find higher agreement across methods by estimating the most important
spans dynamically instead of relying on a fixed subset of size $k$. We
systematically investigate the interaction between $k$ and spans and propose an
improved configuration for selecting important tokens.

通过研究不同解释方法对词汇重要性的选择差异，我们发现选择不同类别的词汇会导致方法的分歧，而与其他方法和人类的最一致的方法更倾向于选择类似的语言偏好，通过在句法范围级别上比较可以减少方法间的差异，通过动态预估最重要的范围而不是依赖于固定的部分，方法间的一致性较高，我们还提出了一种改进的配置来选择重要的词汇。