A Report On A Mock E-C Simultaneous Interpretation Guided By Syntactic Linearity Based On Graph Similarity And Classification

Artificial Intelligence is a success of worldwide significance to humankind,with which our daily life is deeply concerned.Artificial Intelligence holds the destiny of humans no matter where it is,in intelligent manufacturing and future employment both.Translation,especially interpretation,plays a crucial role in introducing expertise from the advanced powers to China,seeing room for improvement with the expertise interpretation.This report is based on the mock simultaneous interpreting task of the lecture with the name of Graph Similarity and Classification delivered by Professor Michalis Vazirgiannis at Tsinghua University in 2019.This corpus,a lecture of big-data expertise given by a French featuring professionalism,dense information,a fast rhythm,and a thick accent,is 58 minutes approximately.Therefore,documents relating to the progress and application of graph kernel on the speaker’s website are collated.Meanwhile,the data and terms directly linked to the lecture are organized,whereby the author gets familiar with the topic.Then this mock SI begins guided by Syntactic Linearity combined with Goldman-Eisler’s trunking techniques and Kevin Lin’s three-word interpretation.When it is over,in light of Gile’s Effort Model,from the angles of trunking and cohesion,ways of interpreting different words and sentences are analysed in the form of case analysis.In addition,it is described that difficulties including slow speeds,distraction,and unfamiliar topics,and the solutions as well as implications such as “muscle memory” training,corpora expanding,and excellent preparations.This report details the complete progress of this mock interpreting task and analyses some specific examples for exploring the way of integrating theory with practice.It is hoped to provide some more tips for interpreting practices and researches.