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

Exploring the State of the Art in Legal QA Systems