摘要
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.
