一个适用于税法蕴涵和问答的数据集
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
摘要：
立法可以被看作是用自然语言表达的规范性规则。将立法应用于案件事实，我们称之为法定推理，其中这些事实也用自然语言表达。计算法定推理与大多数现有的机器阅读工作不同，因为判决案件所需的大部分信息只声明一次（法律），而机器阅读所需的大部分信息往往是通过分布式语言统计学来学习的。为了研究自然语言理解方法在法律推理中的性能，我们引入了一个数据集，以及一个法律领域的文本语料库。机器阅读模型的直接应用在我们的问题上表现出较低的开箱即用性能，无论它们是否已被微调到法律领域。我们将其与手工构建的基于Prolog的系统进行对比，该系统旨在完全解决任务。这些实验支持的法定推理前进所面临的挑战的讨论，我们认为这是一个有趣的现实世界的任务，可以激励模型的发展，能够利用规定的规则指定的自然语言。
英文摘要：
Legislation can be viewed as a body of prescriptive rules expressed in natural language. The application of legislation to facts of a case we refer to as statutory reasoning, where those facts are also expressed in natural language. Computational statutory reasoning is distinct from most existing work in machine reading, in that much of the information needed for deciding a case is declared exactly once (a law), while the information needed in much of machine reading tends to be learned through distributional language statistics. To investigate the performance of natural language understanding approaches on statutory reasoning, we introduce a dataset, together with a legal-domain text corpus. Straightforward application of machine reading models exhibits low out-of-the-box performance on our questions, whether or not they have been fine-tuned to the legal domain. We contrast this with a hand-constructed Prolog-based system, designed to fully solve the task. These experiments support a discussion of the challenges facing statutory reasoning moving forward, which we argue is an interesting real-world task that can motivate the development of models able to utilize prescriptive rules specified in natural language.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
