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Legislation can be viewed as a body of prescriptive rules expressed innatural language. The application of legislation to facts of a case we refer toas statutory reasoning, where those facts are also expressed in naturallanguage. Computational statutory reasoning is distinct from most existing workin machine reading, in that much of the information needed for deciding a caseis declared exactly once (a law), while the information needed in much ofmachine reading tends to be learned through distributional language statistics.To investigate the performance of natural language understanding approaches onstatutory reasoning, we introduce a dataset, together with a legal-domain textcorpus. Straightforward application of machine reading models exhibits lowout-of-the-box performance on our questions, whether or not they have beenfine-tuned to the legal domain. We contrast this with a hand-constructedProlog-based system, designed to fully solve the task. These experimentssupport a discussion of the challenges facing statutory reasoning movingforward, which we argue is an interesting real-world task that can motivate thedevelopment of models able to utilize prescriptive rules specified in naturallanguage.

A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering