Quantified Assessment of Translation Semantic Quality Based on Neural Network Model
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.