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Existing self-explaining models typically favor extracting the shortestpossible rationales - snippets of an input text "responsible for" correspondingoutput - to explain the model prediction, with the assumption that shorterrationales are more intuitive to humans. However, this assumption has yet to bevalidated. Is the shortest rationale indeed the most human-understandable? Toanswer this question, we design a self-explaining model, LimitedInk, whichallows users to extract rationales at any target length. Compared to existingbaselines, LimitedInk achieves compatible end-task performance andhuman-annotated rationale agreement, making it a suitable representation of therecent class of self-explaining models. We use LimitedInk to conduct a userstudy on the impact of rationale length, where we ask human judges to predictthe sentiment label of documents based only on LimitedInk-generated rationaleswith different lengths. We show rationales that are too short do not helphumans predict labels better than randomly masked text, suggesting the need formore careful design of the best human rationales.

Are Shortest Rationales the Best Explanations for Human Understanding?