A Report On Cat In Popular Science Text Translation

With the development of computer and artificial intelligence,Neural Network Machine Translation system has been available for a long time,which helps translators improve translation efficiency and brings unprecedented challenges to the translation profession as well.However,machine translation still has a lot of disadvantages at present.It is impossible for machines to complete translation projects independently and the translation process still requires human’s participation.Therefore,“Machine Translation(MT)+ Post-editing(PE)” will become the main translation mode in the future.However,“MT+PE” model is not working for all texts translation.According to Katharina Reiss,a German functionalist translation theorist,texts consist of three functions: “informative”,“expressive” and “appealing”.Informative texts aim to convey information,knowledge,arguments and other facts(Reiss,1971).Therefore corpus-based machine translation is more applicable for translating informative texts.The Elements,to some extent,belongs to informative texts.In this translation report the author adopted the “MT+PE” model to complete the translation task of The Elements,a science popular text,and analyzed the first two chapters of the translated version by “MT+PE”.The errors in machine translation were classified as inaccurate terminology,ambiguous pronouns,lack of cohesion,lack of logic,etc.The author finally explored and summarized the translation strategies for “MT+PE”mode in science popular text from the aspect of title,vocabulary and sentence.This article sets out to provide strategies and references for other translators engaged in this field.