Machine Translation Technology Development And Application Prospects In The Big Data Era

The material used in this report is an online lecture given by professor Zhang,an associate professor of Northeastern University(NEU).Mr.Zhang is responsible for the technological and marketing promotion of NiuTrans,a machine translation solution developed by Northeastern University.NiuTrans is developed independently by NiuTrans team of NEU,committed to building the "engine" of machine translation products,providing enterprise users with multi-lingual,high-quality,vertical and personalized machine translation services.The speech,aided only by demonstration slides,was improvised and thus highly colloquial.As it covered the subjects ranging across machine translation,computer linguistics and artificial intelligence,there were a huge number of technical terms.To deal with the problems,the author implemented coping strategies accordingly and made a technical terms chart.In the process of simulation practice,the author found out that the problems encountered fell into three categories:information redundancy,parallel structures with a verb-object structure and the gap in the semantic meaning.In dealing with information redundancy,the omission strategy is adopted.The verb-object structure parallel sentences include parallel SVO structures with one subject and zero subject SVO structures.The former parallel structures are interpreted by implementing a strategy of splitting a sentence into multiple parts,while the latter ones are done by transforming them into passive voice structures.As for the semantic gaps,a variety of strategies are adopted such as literal translation,free translation and addition of notes.After the simulation,the interpreter conducted a case study and summarized the problems to work up this report.It is expected that the report and the lessons the author learned from the practice will provide reference and inspiration for the interpretation learners.