A Comparative Dependency Analysis of Human Translation and Machine Translation: A Case Study of English translation of To Live

Abstract

Recent advances in neural machine translation have significantly improved translation quality, yet its ability to handle syntactic complexity in literary texts remains underexplored. This study examines syntactic differences between human and machine translations of a Chinese literary text from the perspective of mean dependency distance. Drawing on one human translation and four machine-generated translations, the analysis compares dependency distance patterns and investigates how sentence length relates to differences across translations. The findings indicate that although both human and machine translations show a general tendency toward syntactic simplification, notable divergences persist between human and machine output. These divergences are unevenly distributed and are closely associated with sentence length, especially in longer sentences. The study suggests that sentence-level restructuring constitutes a key distinction between human and machine literary translation and remains a challenge for current machine translation systems.