The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations

Abstract

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.