The legal domain demands high linguistic precision, complex structural understanding, and careful handling of jurisdictional diversity. This paper investigates the application of advanced Artificial Intelligence (AI) techniques to three critical challenges in legal text processing: inconsistency detection, legal translation, and simplified access through conversational systems. We evaluate the performance of domain-specific transformer models, LegalBERT and Legal RoBERTa, in detecting inconsistencies across multilingual legal corpora, demo strating that LegalBERT consistently outperforms Legal RoBERTa in accuracy, precision, recall, and F1-score. Additionally, we assess two state-of-the-art machine translation models, MarianMT and NLLB-200, on legal translation tasks, with NLLB-200 achieving superior BLEU and ROUGE scores, reflecting better preservation of legal meaning across languages. Our findings highlight the transformative potential of AI-powered tools to enhance legal document analysis, improve translation accuracy, and increase accessibility to legal information. We also discuss limitations related to domain complexity, data availability, and the necessity of human oversight, proposing future directions including multilingual adaptation, hybrid human-AI workflows, and the development of jurisdiction-aware legal chatbots. This work contributes to advancing AI applications in the legal sector, aiming to support legal professionals and empower broader public access to justice.
Paper
An open-access PDF is published at link.springer.com. 44B holds its address, not the file.
Open PDF