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:
https://github.com/abdoelsayed2016/Legal-Question-Answering-Review.

该研究旨在解决法律问答的问题，提供包括 14 个标准数据集和基于深度学习的法律问答模型在内的全面调查。该研究重点讨论了面临的挑战和不足，以及使用的不同方法和技术。