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. QA (Question answering systems) are designed to generate answers to questions asked in human languages. They use 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, they face 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：在法律领域回答问题是复杂的任务，主要由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业知识，这使得即使对于人类专家而言也极具挑战性。问答系统旨在生成以人类语言提出的问题的答案。它们利用自然语言处理来理解问题，并通过信息检索找到相关答案。问答系统在客户服务、教育、研究和跨语言交流等多个领域具有实际应用。然而，它们面临改进自然语言理解和处理复杂模糊问题等挑战。目前，缺乏专门讨论法律问答领域的综述性文章。为解决这一问题，我们提供了一篇全面综述，回顾了法律领域中问答任务的14个基准数据集，并系统梳理了当前最先进的法律问答深度学习模型。我们涵盖了这些研究中采用的不同架构和技术，以及这些模型的性能与局限性。此外，我们建立了一个公开的GitHub仓库，定期更新最新论文、开放数据和源代码。仓库地址为：\url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}。