How Does the Post-Editing of Neural Machine Translation Compare with From-Scratch Translation? A Product and Process Study

Yanfang Jia, Michael Carl, Xiangling Wang

Research output: Contribution to journalReviewResearchpeer-review

Abstract

This study explores the post-editing process when working within the newly introduced neural machine translation (NMT) paradigm. 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. We analysed translation process and translation product data from 30 first-year postgraduate translation students. The analysis is based on keystroke logging, screen recording, questionnaires, retrospective protocols and target-text quality evaluations. 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. 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.
Original languageEnglish
JournalJournal of Specialised Translation
Issue number31
Pages (from-to)60-86
Number of pages27
ISSN1740-357X
Publication statusPublished - Jan 2019
Externally publishedYes

Keywords

  • Post-editing process
  • Translation quality
  • Neural machine translation
  • Text types

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