How are neural machine-translated Chinese-to-English short stories constructed and cohered? An exploratory study based on theme-rheme structure

Abstract

Using theme-rheme structure in Systemic Functional Linguistics as an analytical framework, this study explores how neural machine-translated Chinese-to-English literary texts are organized and developed in terms of discourse coherence. To this end, we built a comparable translational corpus containing English translations of two modes, viz. neural machine translation (NMT) and human translation, of modern and contemporary Chinese short stories as source texts. The translated texts were annotated with themes, rhemes and their progressions throughout the texts. By analyzing and contrasting their thematic structure and progression patterns, we found that, compared with human translations, 1) NMT texts feature “pseudo-coherence” characterized by ill-connected information fragments linked by overusing and; 2) NMT system fails to adapt the organization of its textual elements to a given purpose and is less competent in maintaining discourse coherence due to a lack of smooth connection to the preceding sentence; 3) NMT system produces static and poorly interconnected texts that read as disjointed as a list consisting of unrelated items by recurrently resorting to a constant progression pattern rather than a dynamic linear progression characteristic of human translation. Hence, we suggest that theme-rheme structure be employed to assess the construction and coherence of machine-translated texts and be incorporated into machine translation models to improve the coherence of their products.