Xueqiu Peng
ABSTRACT
In order to improve the ability of error correction in English translation, this paper proposed a translation error correction model based on differential fusion of syntactic features. Using the syntactic encoder, the dependency and syntactic features of the text to be translated were obtained, and the syntactic feature vector was got by feature fusion. The difference fusion method was used to fuse the grammatical features and syntactic features in the text, and the collaborative attention mechanism was further used to further improve its error correction ability. Conll-2014 data set was used as the test set of the model. The accuracy of the model in this paper reached 70.4%, the recall rate reached 38.4%, and the f0.5 index reached 60.7%. Compared with the syntax error correction model of the standard copy augmented transformer, the F0.5 index of the model in this paper improved by 5.1%. Compared with other models, this model has obvious advantages and important application value.
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Index Terms
- Design of translation error correction model based on differential fusion of syntactic features
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