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.

我们引入了一项新的法律 NLP 任务，通过生成澄清问题来识别合同中的歧义，从而帮助非法律相关的利益相关者获取引出需求所必需的细节。

为消除合同歧义生成澄清问题

Generating Clarification Questions for Disambiguating Contracts

We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive
collection of U.S. federal judicial citations to precedent in context. The
dataset aims to facilitate work on legal passage prediction, a challenging
practice-oriented legal retrieval and reasoning task. Legal passage prediction
seeks to predict relevant passages from precedential court decisions given the
context of a legal argument. We extensively evaluate various retrieval
approaches on LePaRD, and find that classification appears to work best.
However, we note that legal precedent prediction is a difficult task, and there
remains significant room for improvement. We hope that by publishing LePaRD, we
will encourage others to engage with a legal NLP task that promises to help
expand access to justice by reducing the burden associated with legal research.
A subset of the LePaRD dataset is freely available and the whole dataset will
be released upon publication.

我们介绍了法律文书检索数据集 LePaRD，该数据集是美国联邦司法引文的巨大集合，旨在促进法律文书预测的工作。