This study combines semantic variables in existing automatic translation scoring systems with Word2vec and Doc2vec neural network models,and comprehensively compares three types of semantic variables,namely N-gram,semantic points,and text similarity,which includes 14 semantic variables related to the three types of variables in the translation quality evaluation system of explanatory,narrative,and argumentative texts,and constructs a regression model.The results show that:(1)The three types of semantic variables have a certain predictive ability for translation scores,but the correlation coefficients between each variable and translation scores vary;(2)The three types of semantic variables have a certain explanatory power for translation scores,but the regression effect of each individual model is weaker than that of the overall model that includes all variables;(3)The translation quality evaluation models for the three types of text that comprehensively include the three types of semantic variables have a high fitting degree and significant effects.Semantic points and text similarity based on Word2vec contribute more,which is reflected in all three models,while N-gram variables are affected by the characteristics of the source text and have various specific manifestations.
NETL
NSTL
万方数据
