Exploring the state of the art in legal QA systems

Abstract

Abstract 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 more challenging, even for human experts. Question answering (QA) systems are designed to generate answers to questions asked in natural languages. QA uses natural language processing to understand questions and search through information to find relevant answers. 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 discuss the performance and limitations of these models. Moreover, we have established a public GitHub repository that contains a collection of resources, including the most recent articles related to Legal Question Answering, open datasets used in the surveyed studies, and the source code for implementing the reviewed deep learning models (The repository is available at: https://github.com/abdoelsayed2016/Legal-Question-Answering-Review ). The key findings of our survey highlight the effectiveness of deep learning models in addressing the challenges of legal question answering and provide insights into their performance and limitations in the legal domain.

Keywords

• Legal question answering
• Natural language processing
• Machine learning
• Information retrieval
• Legal information extraction
• Transformers