An Empirical Study On The Influence Of Google Translate On Chinese-English Consecutive Interpreting

The development of technologies provides many possibilities for the reality while the communication between people speaking different languages is also becoming more and more frequent in the context of globalization.Demands for effective translation and high-quality interpreting increase.With the emergence of machine translation tools,new options are offered for not only translation but also interpreting.Researchers start to develop ways to combine machine and interpreting with the purpose of improving the efficiency or even replacing interpreters.Speech recognition technology is an important part of this plan,which helps machine recognize words and make interpreting possible.At present,machine involved interpreting is still far from being satisfactory.In light of the popularity of machine translation today,the thesis focuses on one of machine translation tools,GOOGLE TRANSLATE.The possibility of using GOOGLE TRANSLATE in interpreting practices is explored and an empirical research is done to see what GOOGLE TRANSLATE will bring for Chinese-English consecutive interpreting.Gile's Effort Models are adopted as the basis for analyzing the effort allocation of interpreters in consecutive interpreting with and without GOOGLE TRANSLATE and the effects GOOGLE TRANSLATE exerts on Chinese-English consecutive interpreting.The experiment is designed based on the hypothesis that GOOGLE TRANSLATE will improve the performance of interpreters.The overall performance of the subjects confirms the hypothesis.According to the results,interpreters can generally have higher interpreting quality with the help of GOOGLE TRANSLATE.However,individual performance differs.Almost all of the subjects get improved while there are few who become worse.Less competent interpreters are more likely to be affected positively by GOOGLE TRANSLATE.What's more,due to the uneven translation quality,interpreters cannot rely on GOOGLE TRANSLATE too much.