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.

翻译：法定推理是指将立法条文应用于以自然语言描述的一系列案件事实。我们将法定推理重新构建为类比任务，其中每个类比任务实例均涉及两个法定推理实例的组合。这使得数据集规模提升了两个数量级，并引入了可解释性要素。我们证明该任务对自然语言处理模型的难度与原任务大致相当。最后，我们回归法定推理本身，通过结合检索机制与类比模型进行求解，并在与先前可比工作的比较中展现出一定进展。