An Analysis of the Evaluation of the Translation Quality of Neural Machine Translation Application Systems

Abstract

Neural machine translation (NMT) is applied to generate a more reliable and accurate translation practice as the most cutting-edge technology. In recent years, NMT has achieved gratifying results. However, the main obstacle for market-oriented NMT application systems appears to suffer from weak translation quality that fails to meet users’ needs. This paper focuses on the machine translation of political documents and implements six dominant NMT application systems in the market to evaluate their translation quality. The evaluation process further employs both BLEU and NIST technical evaluation algorithms and re-verifies the results with the manual evaluation method called the “Score Ranking System” to compare the performances of the six NMTs in Chinese-English translations of political documents. Through diagnosis and evaluation of the problems and errors in NMTs, the paper eventually proposes the “Cue Lexicon+” model to remedy prominent problems. Besides, the “NMT+ Lexicon Intelligent Translation Assistant” soft is developed and the “Cue Lexicon+” is integrated into the NMT application systems to further improve the translation quality, providing a reference and research basis to increase the performance and update the NMT application systems.