The Comparison of Translationese in Machine Translation and Human
  Transation in terms of Translation Relations
Fan Zhou
This study explores the distinctions between neural machine translation (NMT)
and human translation (HT) through the lens of translation relations. It
benchmarks HT to assess the translation techniques produced by an NMT system
and aims to address three key research questions: the differences in overall
translation relations between NMT and HT, how each utilizes non-literal
translation techniques, and the variations in factors influencing their use of
specific non-literal techniques. The research employs two parallel corpora,
each spanning nine genres with the same source texts with one translated by NMT
and the other by humans. Translation relations in these corpora are manually
annotated on aligned pairs, enabling a comparative analysis that draws on
linguistic insights, including semantic and syntactic nuances such as hypernyms
and alterations in part-of-speech tagging. The results indicate that NMT relies
on literal translation significantly more than HT across genres. While NMT
performs comparably to HT in employing syntactic non-literal translation
techniques, it falls behind in semantic-level performance.