A novel machine translation method for learning chinese as a foreign language

Abstract

It is not easy for western people to learn Chinese. Native German speakers find it difficult to understand how Chinese sentences convey meanings without using cases. Statistical machine translation tools may deliver correct German-Chinese translations, but would not explain educational matters. This article reviews some interdisciplinary research on bilingualism, and expounds on how translation is carried out through cross-linguistic cue switching processes. Machine translation approaches are revisited from the perspective of cue switching concluding that: the word order cue is explicitly simulated in all machine translation approaches, and the case cue being implicitly simulated in statistical machine translation approaches can be explicitly simulated in rule-based and example-based machine translation approaches. A convergent result of machine translation research is to advocate an explicit deep-linguistic representation. Here, a novel machine translation method is motivated by blending existing machine translation methods from the viewpoint of cue-switching, and is firstly aimed as an educational tool. This approach takes a limited amount of German-Chinese translations in textbooks as examples, whose cues can be manually obtained, and for which we have developed MultiNet-like deep linguistic representations and cross-linguistic cue-switching processes. Based on this corpus, our present tool is aimed at helping native German speakers to learn Chinese more efficiently, and shall later be expanded to a more comprehensive machine translation system. © 2014 Springer-Verlag Berlin Heidelberg.