Online Machine Translation Efficiency in Translating Fixed Expressions Between English and Arabic (Proverbs as a Case-in-Point)

Abstract

Doubtless, Machine Translation has affected translation as a process and a product. This study tests MT's effectiveness in translating proverbs between English and Arabic. It investigates one important CAT tool device. It aims to attest which MT will be more communicative, semantic or literal giving target equivalent and clarifying the error type the MT would make. To achieve these aims, thirty proverbs, half Arabic and half English, have been randomly selected, taken from The Dictionary of Common English Proverbs Translated and Explained written by Attia (2004) and then translated using five different online MTs: Google, Reverso, Yandex, Systran, and Bing. As Alabbasi (2015) suggested, the researcher adopted Newmark's (1988) Taxonomy of translation methods, selecting three major divisions that include the other types in one way or another viz. Literal, Semantic and Communicative. Analyzing data, Kruskal-Wallis Test and Chi-square were used as well as descriptive statistics. It is found that the most translation method MT produced when faced with a proverb is the literal, semantic and communicative respectively. Bing is the most effective MT providing communicative proverbial equivalents. Bing and Google, in the same rank, provide semantic equivalents. Furthermore, the least effective MT among the five is Yandex. MT errors diverge between missing the implied meaning, weakly structured translations, wrong synonyms and meaning distorting.