A Study On The Influence Of Google Translate On Chinese-English Consecutive Interpretation From The Perspective Of Gile's Effort Model

The rapid development of artificial intelligence(AI)has brought drastic changes to many fields of our life,including the field of translation and interpretation.In 2016,Google Translate upgraded the underlying technology of its translation system,from the conventional “Phrase-based Machine Translation”(PBMT)to the latest “Google Neural Machines Translation”(GNMT).By equipping Deep Neural Network(DNN),a smart biologically-inspired programming paradigm which enables a computer to learn from observational data,Google greatly enhanced the quality of its translation,enabling machine translation to be one step closer to human translation.The cutting-edge Neural Machine Translation has the potential to overcome many of the weaknesses of conventional phrase-based translation systems.Google's engineers carried out extensive experiments on major pairs of language and published the results in Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation in September 2016.The results show that the new GNMT reduces translation errors by more than 60% compared with the PBMT mode.The improvement provides possibilities of using Google Translate as an auxiliary method during interpretation.The thesis employed Daniel Gile's effort model as the theoretical framework.With its theoretic basis of “limited efforts resource” and its effectiveness in interpreter training practices,effort model has been employed by many books as an important method to study the factors which may affect an interpreter's performance.As GNMT is a relatively new field of study,the combination of effort model and machine translation is rarely seen in past researches.To analyze the auxiliary effect of GNMT from the perspective of effort model may bring some new thoughts for future interpreter's training and cultivation.A Contrast experiment was carried out to study whether Google Translate can help consecutive interpreters' work.Test subjects were asked to do two paragraphs of Chinese-to-English consecutive interpretation.For the first paragraph,they did it as they regularly did.And for the second,they interpreted with the help of Google Translate.The thesis compared the two interpretations of each test subjects from the perspective of accuracy,grammar and fluency.Through qualitative and quantitative analysis,the thesis found that the average accuracy rate of test subjects with Google Translate is raised by 22.7%.In terms of grammatical correctness and fluency,Google Translate's auxiliary effect is limited.The thesis holds the view that at this moment Google Translate's can hardly help human interpreter with their work.The biggest problems preventing Google Translate to be an auxiliary method for interpreters are TL text with no punctuation marks and occasional incorrect translation.These problems result in the performance deterioration because interpreters will have to spend more efforts to make out the incorrect translation.