Post-editing of machine translation (MT) is now increasingly implemented in the human translation workflow after studies in both industry and academia have demonstrated the efficacy of this practice. Post-editing still involves open questions, however, such as how best to train post-editors and how to estimate the effort required by post-editing tasks. In attempting to address some of these questions, many previous studies investigate the post-editing process, but less research has focused on the post-edited product. This chapter examines the link between the process and product of post-editing by checking to see how post-editing effort data related to the quality of post-edited texts, assessed in terms of fluency (linguistic quality) and adequacy (translation accuracy). A statistical analysis indicated that the association between editing operations and the fluency of post-edited texts is dependent on the quality of the raw MT output. Interestingly, a negative association was observed between the number of eye fixations on the text and the quality of the post-edited translations. The chapter shows empirical evidence supporting the distinction between the concepts of translation fluency and adequacy, and postulates that automatic processes play a central role in post-editing performance.
|Title of host publication||Translation in Transition|
|Subtitle of host publication||Between Cognition, Computing and Technology|
|Editors||Arnt Lykke Jakobsen, Bartolomé Mesa-Lao|
|Place of Publication||Amsterdam|
|Publisher||John Benjamins Publishing Company|
|Publication status||Published - 2 Oct 2017|
|Name||Benjamins Translation Library|