Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

Abstract

Machine translation received intensive research in different language pairs; yet, the quality of specialized translation; e.g., legal translation, from Arabic into English received little attention. The present study investigates the quality of machine translation of legal texts from Arabic into English. The paper aims at examining the errors found in the machine translation of legal texts from Arabic into English. It also studies the legal discourse features in machine-translation output. The research questions tackle the accuracy of machine translation of legal discourse and error categories and frequencies in machine translation. The researcher evaluated several factors to assess the quality of Google Translate; i.e., lexical, syntactic, and register-related errors. The study data consists of five legislative texts. The researcher conducted a manual error assessment and classification. To ensure the reliability of the error analysis, an existing human translation of the documents was used as a reference to ensure the reliability of the MT quality assessment and post-editing process. Later, the errors were classified, and their percentages and frequencies were calculated. A few examples of errors in each category were discussed and analysed. The highest error category was lexical errors scoring 43.4%. The last detected error category was deletion with a percentage of 1.7%. Syntactic errors constituted one-fourth of the errors found in the data. Legal register-related errors scored 30.2%. The subcategories of legal register-related errors varied in their occurrence; e.g., one-third of the errors related to legal discourse were legal terms. The study concluded that machine translation; though it provided a comprehensible output, could not translate legal structures and terminology perfectly.