Inner Connection Among Translation Psychology, Probability Theory and Machine Translation
Xin Xiong, Wenxuan Shi, Yixuan Wang, Xiangtong Gao · 2023
The development of the recurrent neural network (RNN) in machine translation (MT) has greatly improved its translation speed and accuracy. Although a larger proportion of the discourse power of MT is increasingly taken in translation activities, MT cannot monopolize human translation due to the probability algorithm. There are two reasons. First, the order and format of some texts will affect the accuracy of machine translation according to the algorithm of probability theory. Second, “memory” is not only an indispensable core technique in machine translation but also an insurmountable component in translation psychology. Although the current research on MT based on RNNs has made some achievements, it is still impossible to obtain the most scientific combination mode of the parts of speech according to probabilistic computing; therefore, the machine itself cannot distinguish the correctness of the source language, which leads to the fact that the input of the source language with the wrong structure may still obtain the correct translation. This paper explores possible ways to optimize MT by researching the internal relations among probability theory, translation psychology and MT.