Can a corpus-driven lexical analysis of human and machine translation unveil discourse features that set them apart?

Abstract There is still much to learn about the ways in which human and machine translation differ with regard to the contexts that regulate the production and interpretation of discourse. The present study explores whether a corpus-driven lexical analysis of human and machine translation can unveil discourse features that set the two apart. A balanced corpus of source texts aligned with authentic, professional translations and neural machine translations was compiled for the study. Lexical discrepancies in the two translation corpora were then extracted via a corpus-driven keyword analysis, and examined qualitatively through parallel concordances of source texts aligned with human and machine translation. The study shows that keyword analysis not only reiterates known problems of discourse in machine translation such as lexical inconsistency and pronoun resolution, but can also provide valuable insights regarding contextual aspects of translated discourse deserving further research.