Construction of German machine translation model combining long short-term memory and multi-subspace attention mechanism
Lingshan Liu · 2025
In the context of rapid development of information technology, the development of intelligent algorithms has greatly promoted the progress of natural language processing technology. This study addresses the inefficiency and gradient problem of the traditional neural machine translation model in dealing with German long sequence data, and proposes a German machine translation model that integrates the hyperbolic tangent long short-term memory network and the multisubspace attention mechanism. The outcomes indicated that the new model has a bilingual evaluation substitution index of 0.98, an explicitly ordered translation evaluation index of 0.99, a translation editing rate of 99.87%, and an average response time of 2.54 seconds, which are all higher than the comparison model and significantly better than the comparison model. It shows faster translation speed and higher stability. In addition to increasing the accuracy of German translation, this study offers new technology for a number of fields that need precise and quick translation.