1. Fine-Grained Evaluation of English to Arabic Neural Machine Translation: A Case Study of Education Research Abstract.
- Author
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Almekhlafi, Hesham A. and Nagi, Khalil A.
- Subjects
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MACHINE translating , *EDUCATION research , *RESEARCH teams , *TRANSLATING & interpreting - Abstract
The study aims to investigate the quality of neural machine translation when translating research paper abstracts from English to Arabic. It performs an error analysis and provides an evaluation of the quality of neural machine translation (NMT) represented by Google Translate and Microsoft Translator. The research team selects 25 English research paper abstracts in education from well-known Scopus scientific journals issued in English speaking countries. These abstracts are then translated into Arabic using both Google Translate and Microsoft Translator. The error analysis is based on the typology of errors introduced by Multidimensional Quality Metrics (MQM). A professional evaluation is also conducted using the Scalar Quality Metric evaluation (SQM) as proposed in Freitag (2021). The study finds that the translation outputs of academic texts like abstracts of education research papers are still not up to standards when translating English educational research abstracts into Arabic. There are various types of translation errors. However, there is a slight difference in translation quality and number of errors in favor of Google Translate compared to Microsoft Translator. However, it is included that NMT system still requires a lot of training, and more Arabic corpora need to be built. [ABSTRACT FROM AUTHOR]
- Published
- 2024