Spurious correlations are a threat to the trustworthiness of natural language
processing systems, motivating research into methods for identifying and
eliminating them. However, addressing the problem of spurious correlations
requires more clarity on what they are and how they arise in language data.
Gardner et al (2021) argue that the compositional nature of language implies
that \emph{all} correlations between labels and individual "input features" are
spurious. This paper analyzes this proposal in the context of a toy example,
demonstrating three distinct conditions that can give rise to feature-label
correlations in a simple PCFG. Linking the toy example to a structured causal
model shows that (1) feature-label correlations can arise even when the label
is invariant to interventions on the feature, and (2) feature-label
correlations may be absent even when the label is sensitive to interventions on
the feature. Because input features will be individually correlated with labels
in all but very rare circumstances, domain knowledge must be applied to
identify spurious correlations that pose genuine robustness threats.

该论文分析了 Gardner 等人提出的语言的组合性质意味着标签和各自的‘输入特征’之间的所有相关性都是虚假相关性这一提议，在简单的 PCFG 中演示出三种明显的条件可以导致特征 - 标签相关性的产生，论文指出在所有但极少数情况下，输入特征将与标签各自相关，因此需应用领域知识识别可能对鲁棒性构成真正威胁的虚假相关。