Abstract
The neural machine translation (NMT) revolution is upon us. Since 2016, an increasing number of scientific publications have examined the improvements in the quality of machine translation (MT) systems. However, much remains to be done for specific language pairs, such as Arabic and English. This raises the question whether NMT is a useful tool for translating text from English to Arabic. For this purpose, 100 English passages were obtained from different broadcasting websites and translated using NMT in Google Translate. The NMT outputs were reviewed by three professional bilingual evaluators specializing in linguistics and translation, who scored the translations based on the translation quality assessment (QA) model. First, the evaluators identified the most common errors that appeared in the translated text. Next, they evaluated adequacy and fluency of MT using a 5-point scale. Our results indicate that mistranslation is the most common type of error, followed by corruption of the overall meaning of the sentence and orthographic errors. Nevertheless, adequacy and fluency of the translated text are of acceptable quality. The results of our research can be used to improve the quality of Google NMT output.
Highlights
In the past years, the translation process has substantially changed because of technological advancements, such as the use of Internet and the availability of web-based machine translation (MT) systems (Johnson et al, 2017)
We provide a detailed overview of the types of errors in Google neural machine translation (GNMT) and identify its potential shortcomings using (1) human evaluation of adequacy and fluency and (2) human error analysis methods
In terms of the quality assessment (QA) of adequacy and fluency, the results were 70% for accuracy and 77% for fluency. According to these results for English-to-Arabic translation, Google Translate produces sentences with relatively few errors, and the translated text is fluent to some extent
Summary
The translation process has substantially changed because of technological advancements, such as the use of Internet and the availability of web-based machine translation (MT) systems (Johnson et al, 2017). MT is an approach to translating texts from one language to another. MT had a poor reputation because its output was perceived to be of low quality (e.g., Agarwal et al, 2011). Recent research has found that the quality of output has improved enough to be used in the translation industry (e.g., Chen, Acosta, & Barry, 2016). MT has been developed since the 1950s, and different theories and practices have emerged over time. The quality of neural machine translation (NMT) has been the primary concern of researchers. NMT has emerged as an innovative translation approach that uses deep learning for translation of text in foreign languages (Wu et al, 2016)
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