  Statutory reasoning refers to the application of legislative provisions to a
series of case facts described in natural language. We re-frame statutory
reasoning as an analogy task, where each instance of the analogy task involves
a combination of two instances of statutory reasoning. This increases the
dataset size by two orders of magnitude, and introduces an element of
interpretability. We show that this task is roughly as difficult to Natural
Language Processing models as the original task. Finally, we come back to
statutory reasoning, solving it with a combination of a retrieval mechanism and
analogy models, and showing some progress on prior comparable work.
