Towards a Literary Machine Translation: The Role of Referential Cohesion

Abstract

What is the role of textual features above the sentence level in advancing the machine translation of literature? This paper examines how referential cohesion is expressed in literary and non-literary texts and how this cohesion affects translation. We first show in a corpus study on English that literary texts use more dense reference chains to express greater referential cohesion than news. We then compare the referential cohesion of machine versus human translations of Chinese literature and news. While human translators capture the greater referential cohesion of literature, Google translations perform less well at capturing literary cohesion. Our results suggest that incorporating discourse features above the sentence level is an important direction for MT research if it is to be applied to literature.