Incorporating Human Translator Style into English-Turkish Literary
  Machine Translation
Zeynep Yirmibeşoğlu Olgun Dursun Harun Dallı Mehmet Şahin Ena Hodzik Sabri Gürses Tunga Güngör
Although machine translation systems are mostly designed to serve in the
general domain, there is a growing tendency to adapt these systems to other
domains like literary translation. In this paper, we focus on English-Turkish
literary translation and develop machine translation models that take into
account the stylistic features of translators. We fine-tune a pre-trained
machine translation model by the manually-aligned works of a particular
translator. We make a detailed analysis of the effects of manual and automatic
alignments, data augmentation methods, and corpus size on the translations. We
propose an approach based on stylistic features to evaluate the style of a
translator in the output translations. We show that the human translator style
can be highly recreated in the target machine translations by adapting the
models to the style of the translator.