Evaluation Methods of Machine Translation Based on Big Data Algorithms
Bing Zhang · 2024
In order to solve the challenges of machine translation evaluation methods, this study proposes an innovative machine translation evaluation method based on big data algorithms in view of the shortcomings of existing dynamic programming algorithms. This new scheme uses the principle of overlapping sub-problem theory to accurately identify and locate the key influencing factors, and accordingly carries out a wise classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of big data algorithms, this scheme cleverly constructs the design strategy of translation evaluation methods. The empirical results show that the proposed scheme shows a significant improvement over the traditional dynamic programming algorithm in terms of the accuracy of the machine translation evaluation method and the processing efficiency of key factors, showing its obvious strong advantages. Machine translation evaluation methods play a crucial role in translation systems, which can accurately predict and optimize the growth trend and output results of machine translation evaluation methods. However, in the face of complex simulation tasks, traditional dynamic programming algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of machine translation evaluation method optimized by big data algorithms, and accurately controls the influencing parameters through the overlapping sub-problem theory, and uses this as a road map for index allocation, and then uses big data algorithms to innovate and construct a system scheme.