改进粒子群BP算法的四六级翻译评分模型

针对四六级考试翻译题型, 给出了一种基于改进PSO-BP神经网络的评分方法. 通过BLEU和SVD等算法获取到文本特征值以及老师评分作为输入集, 然后用该集合对改进PSO-BP神经网络进行训练, 训练好的BP神经网络可以用来预测翻译分数. 从惯性权值计算和适应度函数两方面优化了PSO-BP算法, 在全局范围内寻找最优解, 使得实验效果更加稳定. 用Matlab进行了仿真实验, 结果表明, 在翻译评分中, 使用改进PSO-BP神经网络比采用多元线性回归能获得更好的相关性, 与人工评分的皮尔逊相关系数平均提高了12%.

Translation Scoring Model in CET Based on Improved PSO-BP Nerual Network

This paper presents a new scoring method for the translation in CET-4 and CET-6 based on improved PSO-BP neural network. We get the text feature values by using the algorithm of BLEU and SVD, together with the scores the teacher have scored, are gathered as input set. We use it to train the improved PSO-BP neural network, which can be reversely used to predict the translation score. This paper improves the PSO-BP neural network by the calculation of the inertia weight and the adaptive value function. We use Matlab to make simulation, the result shows that, in the translation scoring, the use of improved PSO-BP neural network is better than using multiple linear regression to obtain better correlation, and the Pearson correlation coefficient of artificial scoring average increased by 12%.