Application of Fusion Differential Evolution Algorithm based on Improved Gaussian Mixture Model in Translation Evaluation

In recent years, with the progress and popularization of science and technology, Natural Language Generation (NLG) represented by machine translation has gradually become familiar to human beings, and human beings rely more and more on it for their daily work and life. On this basis, an evaluation method based on NLG system is proposed. The traditional method based on manual evaluation, with its long evaluation period and high cost, seriously restricts the efficiency of machine translation evaluation, thus hindering the development of English evaluation. In the NLG automated evaluation system, the textual linguistic information is processed so as to achieve the understanding of the sentence information and to evaluate it equivalently. At present, the continuous development and improvement of automated evaluation algorithms have greatly accelerated the development of NLG system developers, and the system has begun to come into people's daily life without the need for repeated manual evaluation. This paper introduces a fusion differential evolutionary algorithm based on an improved Gaussian mixture model for enhancing the accuracy of translation evaluation. The method effectively captures the complex distributional features of translation quality scores by introducing a Gaussian mixture model, and combines the global optimization capability of the differential evolution algorithm in order to optimize the parameters of the translation evaluation model. The experimental results show that the algorithm proposed in this paper exhibits higher accuracy and robustness in evaluating translation quality compared with traditional methods. By testing the multilingual translation dataset, this study verifies the effectiveness and wide applicability of the algorithm in practical applications.