Multidimensional comparison of Chinese-English interpreting outputs from human and machine: Implications for interpreting education in the machine-translation age

Abstract

Interpreting (i.e., oral translation) is facing challenges due to the prevalence of machine translation, and one urgent question for interpreting educators is what knowledge and skills should be taught in the machine-translation age. Focusing on this issue, the present corpus-based study systematically quantified the differences between interpreting outputs from expert interpreters and two machine translation systems. Using text analysis tools, significant differences in multidimensional linguistic features including lexical, syntactic, and cohesive ones were shown between humans and machines but not between two artificial systems. Finer-grained statistical analyses indicated that human-machine differences in certain indices deviated in varied interpreting modes. Our data collectively revealed the strengths of human interpreters in audience-oriented communicative mediation but limitations in cognitive resources. By relating the findings to interpreting competence, the current research provides important implications for empowering students in adaptively resorting to human strengths and/or embracing machine translation technologies.