Reframing Tax Law Entailment as Analogical Reasoning
This addresses the problem of statutory reasoning in tax law for NLP applications, but it is incremental as it builds on prior comparable work.
The authors tackled statutory reasoning in tax law by reframing it as an analogical reasoning task, which increased dataset size by two orders of magnitude and showed the task is similarly difficult for NLP models, leading to some progress on prior work through a retrieval and analogy model approach.
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.