When machines meet gavel: a case study of the English–Arabic machine translation of the Egyptian arguments before the International Court of Justice (2024)

Abstract The legal field heavily relies on audio–visual content such as witness testimonies and trials, making accurate transcription and translation crucial, especially in cross-border cases. This study examines the performance of neural machine translation (NMT) in handling such material, using the DQF-MQM harmonized error typology to categorize errors by type, including terminology, accuracy, and fluency. Legal translation demands precision, as minor errors can impact legal outcomes. Thus, this analysis focuses on English-to-Arabic translations of Egyptian oral arguments before the International Court of Justice, sourced from DawnNews (Feb 21, 2024). It investigates whether errors stem from the ASR-generated transcript or the Google NMT system. The findings aim to guide machine translation post-editors (MTPEs) in identifying lexical and syntactic patterns that typically result in errors, ultimately supporting more accurate and legally sound translations.