Post-editing Neural Machine Translation versus Phrase-based Machine Translation for English–Chinese

Yanfang Jia, Michael Carl, Xiangling Wang

Research output: Contribution to journalJournal articleResearchpeer-review


This paper aims to shed light on the post-editing process of the recently-introduced neural machine translation (NMT) paradigm. Using simple and more complex texts, we first evaluate the output quality from English to Chinese phrase-based statistical (PBSMT) and NMT systems. Nine raters assess the MT quality in terms of fluency and accuracy and find that NMT produces higher-rated translations than PBSMT for both texts. Then we analyze the effort expended by 68 student translators during HT and when post-editing NMT and PBSMT output. Our measures of post-editing effort are all positively correlated for both NMT and PBSMT post-editing. Our findings suggest that although post-editing output from NMT is not always significantly faster than post-editing PBSMT, it significantly reduces the technical and cognitive effort. We also find that, in contrast to HT, post-editing effort is not necessarily correlated with source text complexity.
Original languageEnglish
JournalMachine Translation
Issue number1-2
Pages (from-to)9-29
Number of pages21
Publication statusPublished - 8 Mar 2019
Externally publishedYes

Bibliographical note

Epub ahead of print. Published online: 8. March 2019


  • Neural machine translation
  • Phrase-based statistical machine translation
  • Temporal effort
  • Technical effort
  • Cognitive effort
  • Human assessment

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