Computational Statutory Reasoning

税法是一篇庞大而复杂的文本，与庞大而复杂的美国经济的征税方式类似。所有三个政府部门都在不断增加新的文本：国会增加税法，美国国税局发布解释，法院撰写税务案件的裁决。对任何一个人来说，了解所有税法即使不是不可能，也是具有挑战性的。这可能会导致完全明智的税法当局以作者意想不到的方式进行互动，使个人和公司与聪明的税务顾问一起使用避税策略。这样的策略花费了政府数十亿美元，并助长了公众对税收不公平的看法。开发能够自动理解税法文本并进行推理的人工智能(AI)将有两个好处。首先，可以确定并关闭现有税法可能采取的避税策略。其次，新税法文本的创建者(国会工作人员、美国国税局律师和在税务案件中撰写意见的法官)可以验证他们没有无意中启用新的避税策略。这个项目的目的是开发工具来自动理解和推理税法文件。这包括税收法规和判例法。主要的研究问题是如何推理哪些法规适用于给定的案件，新的法规如何潜在地影响以前已裁决的案件，以及如何自动确定一个案件是否构成另一个案件的先例。首先，该项目将建立基准数据集，以衡量在上述研究目标方面取得的进展，依靠现有的数据集管理专业知识和公开的法律数据。其次，最近在将文本数据转换为支持自动推理的结构方面的进展需要扩展到法律领域。与将文本提取到数据相比，这将需要在将语言(法规)映射到机器可解释规则方面进行创新。第三，这个项目将开发法律领域本体、模式和信息提取模型来分析美国判例法。在分析法规和案例方面的进展将涉及扩展语义解析、实体类型化、共指关系、注释科学、模式归纳和推理、人工智能系统工程、文本推理和领域专门语言模型预培训等领域的能力。这一努力将带来关于法律语言创造和使用的新思维方式，自然语言处理和自动推理方面的进步，特别是在极少机会学习领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Tax law is a huge, complex body of text, paralleling how the huge, complex U.S. economy is taxed. All three branches of government continually add new text: Congress adds to the Tax Code, the IRS issues interpretations, and courts write decisions in tax cases. It is challenging if not impossible for any single human to be aware of all of tax law. This can lead to entirely-sensible tax-law authorities interacting in ways unforeseen by their authors, enabling tax-avoidance strategies used by individuals and corporations with clever tax advisors. Such strategies cost the government billions of dollars and feed public perceptions of tax unfairness. Developing artificial intelligence (AI) that can automatically understand and reason with tax-law text would have two benefits. First, tax-avoidance strategies possible with existing tax law could be identified and shut down. Second, creators of new tax-law text (congressional staffers, IRS attorneys, and judges writing opinions in tax cases) could verify that they were not inadvertently enabling new tax-avoidance strategies. The aim of this project is to develop tools to automatically understand and reason with tax-law documents. This includes tax statutes and case law. The main research questions are how to reason about which statutes apply to a given case, how new statutes potentially impact previous decided cases, and how to automatically determine whether one case constitutes precedent for another case. First, this project will build benchmark datasets to measure progress on the above research goals, relying on existing expertise in dataset curation and on open legal data. Second, recent progress on converting textual data to structures supporting automated reasoning needs to be extended to the legal domain. This will require innovations in mapping language (statutes) into machine interpretable rules as compared to extracting text into data. Third, this project will develop legal domain ontologies, schemas, and information extraction models to analyze US case law. Progress on analyzing statutes and cases will involve extending capabilities in areas such as semantic parsing, entity typing, coreference, annotation science, schema induction and inference, AI system engineering, textual inference, and domain specialized language model pre-training. The effort will lead to new ways of thinking about the creation and use of legal language, with advances in natural language processing and automated reasoning, especially in the area of few-shot learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.