An Empirical Analysis of Neural Network Machine Translation and Human Translation

This study explores the performance and sentiment analysis differences between machine translation and human translation in various fields from a quantitative perspective. The findings reveal that: 1. Human translation performs better than machine translation across all fields, but the gap between machine and human translation is smaller in daily language. 2. In highly creative fields such as literature, there is still a significant gap between machine translation and human translation. 3. In specialized fields like economy, trade, and business, which have a high volume of professional vocabulary, the accuracy of machine translation may decrease if the text database is not updated promptly. 4. Machine translation is in urgent need of improvement in aspects such as vocabulary richness, expression flexibility, complex sentence recognition, sentiment analysis and expression, and translation methods. Overall, this study not only deepens the path of empirical translation research but also offers insights for the study of translation technology.