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Exploring the State of the Art in Legal QA Systems

arXiv 2304.06623 ↗First posted 13 Apr 2023cs.CLcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 94 references that are in the map. First posted on arXiv 13 Apr 2023.

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21 of 94 references.

References · 21

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9 more
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