Research on Neural Machine Translation Systems Integrating Multi granularity Morphological Features in the Context of Artificial Intelligence Translation
In today's era of economic globalization,neural machine translation is widely used in translation work for various lan-guages.However,due to the complexity of the language itself and differences in cultural backgrounds,there is a problem of low trans-lation quality.Therefore,this study proposes a neural machine translation system that combines multi granularity morphological fea-tures with deep encoder information to fuse multi granularity morphological features.The research results show that in the comparison of TOP 1 and TOP2 scores,the neural machine translation model fused with multi granularity morphological features has the highest scores in both terms,with 45.63 and 49.06 respectively.And the average translation speed of the system proposed in the study is 0.3 sentences/s.In summary,the translation model and system proposed in the study can achieve good translation results in a relatively short period of time and effectively address the urgent needs of the current society.
artificial intelligencemulti granularitymorphological characteristicsneural machine translation systemdeep en-coder information
NETL
NSTL
万方数据
