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Unfortunately, raw MT output cannot always meet the end user’s expectations in terms of translation quality, thus making MT plus post-editing a necessary and standard practice.
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In the last decade, machine translation (MT) has been increasingly adopted by the translation industry as an effective solution to the globally ever-increasing demands for translation that from-scratch human translation cannot satisfy. Post-editing process, translation quality, neural machine translation, text types. These were mainly due to the influence of their previous translation training, lack of experience in post-editing and the ambiguous wording of the post-editing guidelines. The three main findings were: 1) post-editing GNMT was only significantly faster than from-scratch translation for domain-specific texts, but it significantly reduced the participants’ cognitive effort for both text types 2) post-editing GNMT generated translations of equivalent fluency and accuracy as those generated by from-scratch translations 3) the student translators generally showed a positive attitude towards post-editing, but they also pointed to various challenges in the post-editing process. The analysis is based on keystroke logging, screen recording, questionnaires, retrospective protocols and target-text quality evaluations. We analysed translation process and translation product data from 30 first-year postgraduate translation students. To this end, an experiment was carried out to examine the differences between post-editing Google neural machine translation (GNMT) and from-scratch translation of English domain-specific and general language texts to Chinese. This study explores the post-editing process when working within the newly introduced neural machine translation (NMT) paradigm. Xiangling Wang, Hunan University ABSTRACT How does the post-editing of neural machine translation compare with from-scratch translation? A product and process study Yanfang Jia, Hunan University