  Existing self-explaining models typically favor extracting the shortest
possible rationales - snippets of an input text "responsible for" corresponding
output - to explain the model prediction, with the assumption that shorter
rationales are more intuitive to humans. However, this assumption has yet to be
validated. Is the shortest rationale indeed the most human-understandable? To
answer this question, we design a self-explaining model, LimitedInk, which
allows users to extract rationales at any target length. Compared to existing
baselines, LimitedInk achieves compatible end-task performance and
human-annotated rationale agreement, making it a suitable representation of the
recent class of self-explaining models. We use LimitedInk to conduct a user
study on the impact of rationale length, where we ask human judges to predict
the sentiment label of documents based only on LimitedInk-generated rationales
with different lengths. We show rationales that are too short do not help
humans predict labels better than randomly masked text, suggesting the need for
more careful design of the best human rationales.
