Cross-Language and Multilingual Document Integrity

Abstract----As legal systems become increasingly globalized, it has become necessary to accurately translate legal documents in different languages. However, translation inconsistencies can create legal disputes and misinterpretations. This paper investigates the application of machine learning (ML) methods, specifically Long Short-Term Memory (LSTM) networks, Random Forest (RF) classifiers, and Natural Language Processing (NLP) methods, to detect inconsistencies in multilingual legal documents. By using deep learning and semantic analysis, we propose a system to ensure translation fidelity and multi-language document integrity. The paper describes the design, development, strengths, limitations, and future directions of cross-language verification in legal technology platforms like Lex Net.