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This study seeks to uncover the types of errors produced by the MTs in refugee news headlines from English into Arabic, with reference Costa’s model of error analysis 2014. This research will be of valuable scientific benefit of university students in general and translation students in particular. The results of this study were used as a guide for writing a thesis summary.
Four types of error classification were used: grammatical, lexical, semantic, and orthography. This study sheds light on the indispensable role of the human translator in correcting the errors that machine translation systems face in translating political terms, abbreviations, proper nouns, and others in refugee news headlines and revealing the best application of a mechanism among the selected systems that is closer to human translation and which has been able to overcome the most grammatical, semantic, lexical and spelling.
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