Edited by Frans De Laet, In-kyoung Ahn and Joong-chol Kwak
[Babel 66:4/5] 2020
► pp. 811–828
The rapid development of neural machine translation systems and the emergence of the e-book have broadened the scope of text types that can be translated by machines. At the early stage of the machine’s infiltration into the translation field, target texts were mainly technical texts such as patents, instruction manuals, etc. Literary texts have been considered as the last bastion of human translation because the machine translation (MT) has produced word-for-word translation, unsuitable for literary texts with distinct stylistic elements. However, it turns out that the field of literary translation was not immune to the rise of MT. Style is one of the critical elements in literary texts, but it has been dismissed in the existing MT post-editing guidelines. Therefore, this research attempts to provide methodological ideas about how to come up with a machine translation post-editing guideline (MTPE) for style improvement especially for language pairs with divergent syntax and semantics like English and Korean. First, the linguistic and cultural differences in writing styles are sorted out based on previous research. Second, the different ways in which human translators address writing style are investigated. Third, the strategies that human translators employ in their translations are applied to machine translation post-editing to demonstrate how the strategies can be incorporated into English-Korean MTPE to improve style. This preliminary research would lay the groundwork for refining post-editing style guidelines and for accumulating manually post-edited data for style improvement, which would be conducive to building and customizing automatic post-editing systems.