Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. Question answering (QA) systems are designed to generate answers to questions asked in human languages. QA uses natural language processing to understand questions and search through information to find relevant answers. QA has various practical applications, including customer service, education, research, and cross-lingual communication. However, QA faces challenges such as improving natural language understanding and handling complex and ambiguous questions. Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. Moreover, we have established a public GitHub repository where we regularly upload the most recent articles, open data, and source code. The repository is available at: \url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}.

翻译：回答法律领域的问题是一项复杂任务，主要源于法律文件系统的复杂性和多样性。为法律查询提供准确答案通常需要相关领域的专门知识，这使得该任务即便对人类专家也极具挑战性。问答系统旨在生成以人类语言提出的问题的答案，它利用自然语言处理来理解问题并搜索信息以找到相关答案。问答系统具有多种实际应用，包括客户服务、教育、研究和跨语言交流。然而，问答系统面临诸多挑战，例如提升自然语言理解能力以及处理复杂且模糊的问题。当前，缺乏讨论法律问答的综述性研究。为解决这一问题，我们提供了一份全面综述，回顾了法律领域问答的14个基准数据集，并全面介绍了最新的法律问答深度学习模型。我们涵盖了这些研究中使用的不同架构和技术，以及这些模型的性能和局限性。此外，我们建立了一个公开的GitHub仓库，定期上传最新文章、开放数据和源代码。该仓库地址为：\url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}。