Making a (Counterfactual) Difference One Rationale at a Time. (arXiv:2201.05177v1 [cs.CL])

Rationales, snippets of extracted text that explain an inference, have
emerged as a popular framework for interpretable natural language processing
(NLP). Rationale models typically consist of two cooperating modules: a
selector and a classifier with the goal of maximizing the mutual information
(MMI) between the "selected" text and the document label. Despite their
promises, MMI-based methods often pick up on spurious text patterns and result
in models with nonsensical behaviors. In this work, we investigate whether
counterfactual data augmentation (CDA), …

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