Machine Translation System Based on Deep Learning
Ting Yang, Shinan Zhao, He Chen, Bo Chen · Journal of Physics Conference Series · 2021
The translation fusion of multi-machine translation is a strategy that has been proposed very early to improve the quality of machine translation. It has been fully discussed under the framework of statistical machine translation, and further breakthroughs in performance have entered a bottleneck period. Since the fusion methods of existing methods are mostly limited to surface features and lack effective deep fusion strategies, this paper encodes source language sentences and multiple system translations separately to achieve fusion at the coding level. In the specific decoding, we limit the decoding space, so that the translation model can obtain a higher performance improvement on less data. When increasing the values of the two hyper-parameters α and β , the translation effect can be gradually improved. The performance of the model reaches the maximum when a and b are respectively set to 0.4, and the translation performance will decrease if it continues to increase.