Legal documents have complex document layouts involving multiple nested sections, lengthy footnotes and further use specialized linguistic devices like intricate syntax and domain-specific vocabulary to ensure precision and authority. These inherent characteristics of legal documents make question answering challenging, and particularly so when the answer to the question spans several pages (i.e. requires long-context) and is required to be comprehensive (i.e. a long-form answer). In this paper, we address the challenges of long-context question answering in context of long-form answers given the idiosyncrasies of legal documents. We propose a question answering system that can (a) deconstruct domain-specific vocabulary for better retrieval from source documents, (b) parse complex document layouts while isolating sections and footnotes and linking them appropriately, (c) generate comprehensive answers using precise domain-specific vocabulary. We also introduce a coverage metric that classifies the performance into recall-based coverage categories allowing human users to evaluate the recall with ease. We curate a QA dataset by leveraging the expertise of professionals from fields such as law and corporate tax. Through comprehensive experiments and ablation studies, we demonstrate the usability and merit of the proposed system.

翻译：法律文档具有复杂的文档布局，涉及多个嵌套章节、冗长的脚注，并进一步使用复杂的句法和领域特定词汇等专门语言手段以确保精确性和权威性。法律文档的这些固有特性使得问答任务具有挑战性，特别是当问题的答案跨越数页（即需要长上下文）且要求全面详尽时（即长形式答案）。鉴于法律文档的特殊性，本文针对长形式答案背景下的长上下文问答挑战展开研究。我们提出一个问答系统，该系统能够：（a）解构领域特定词汇以改进从源文档中的检索；（b）解析复杂的文档布局，同时分离章节和脚注并适当地建立链接；（c）使用精确的领域特定词汇生成全面的答案。我们还引入了一种覆盖率度量，将性能划分为基于召回率的覆盖率类别，使人类用户能够轻松评估召回情况。我们利用来自法律和公司税务等领域的专业人士的知识，构建了一个问答数据集。通过全面的实验和消融研究，我们证明了所提出系统的实用性和优势。