生成澄清问题以消除合同歧义
Generating Clarification Questions for Disambiguating Contracts
AI总结：
针对非法律利益相关者理解合同困难的问题，提出ConRAP检索增强提示框架，自动生成澄清问题以消除合同歧义，在CUAD数据集上F2分数达0.87，70%问题被评估为有用。
摘要：
企业经常签订商业合同，这些合同可以作为项目特定需求的重要来源。合同条款具有强制性，从合同中衍生的需求可以详细说明非法律利益相关者（包括需求分析师、工程师和交付人员）需要进行的下游实施活动。然而，由于大量使用法律术语以及合同语言固有的复杂性，这些利益相关者理解合同在认知上要求很高且容易出错。此外，合同通常包含措辞模糊的条款以确保全面覆盖。相比之下，非法律利益相关者需要对合同条款有详细且无歧义的理解，才能制定可操作的需求。在这项工作中，我们引入了一个新颖的法律自然语言处理任务，涉及为合同生成澄清问题。这些问题旨在文档级别识别合同歧义，从而帮助非法律利益相关者获得引出需求所需的必要细节。该任务面临三个核心问题：（1）数据可用性，（2）合同的长度和非结构化性质，以及（3）法律文本的复杂性。为了解决这些问题，我们提出了ConRAP，一个检索增强的提示框架，用于生成澄清问题以消除合同文本的歧义。在从公开可用的CUAD数据集获取的合同上进行的实验表明，使用ChatGPT的ConRAP可以以0.87的F2分数检测歧义。70%的生成澄清问题被人类评估者认为有用。
英文摘要：
Enterprises frequently enter into commercial contracts that can serve as vital sources of project-specific requirements. Contractual clauses are obligatory, and the requirements derived from contracts can detail the downstream implementation activities that non-legal stakeholders, including requirement analysts, engineers, and delivery personnel, need to conduct. However, comprehending contracts is cognitively demanding and error-prone for such stakeholders due to the extensive use of Legalese and the inherent complexity of contract language. Furthermore, contracts often contain ambiguously worded clauses to ensure comprehensive coverage. In contrast, non-legal stakeholders require a detailed and unambiguous comprehension of contractual clauses to craft actionable requirements. In this work, we introduce a novel legal NLP task that involves generating clarification questions for contracts. These questions aim to identify contract ambiguities on a document level, thereby assisting non-legal stakeholders in obtaining the necessary details for eliciting requirements. This task is challenged by three core issues: (1) data availability, (2) the length and unstructured nature of contracts, and (3) the complexity of legal text. To address these issues, we propose ConRAP, a retrieval-augmented prompting framework for generating clarification questions to disambiguate contractual text. Experiments conducted on contracts sourced from the publicly available CUAD dataset show that ConRAP with ChatGPT can detect ambiguities with an F2 score of 0.87. 70% of the generated clarification questions are deemed useful by human evaluators.
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。
