Corpus-Based Comparative Study of Google and Youdao Machine Translation Quality
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Xinqi Dun, Shan Liu
In recent years, Machine Translation (MT) has seen great development as well as constant doubts in translation quality. This paper combines corpus research with machine translation text research and then compares translation quality of two machine translation engines, namely, Google and Youdao based on self-built Chinese-to-English translation text corpus mainly from political publicity texts and technical texts, finding the texts that the two translation engines are good at. The research results of this paper are of significant methodological value for the corpus-based comparative study of the translation quality of other translation engines in the future, and practically significant for people to choose a translation engine according to their needs, and shed light on the improvement or professional development of translation engines theoretically.
Corpus, Machine translation quality, Political publicity text, Technical text