An Automatic Error Detection Method for Engineering English Translation Based on the Deep Learning Model

Abstract

Accuracy of deep learning model translation is a key index to evaluate the application performance of engineering English translation. In this paper, an automatic error detection system for English translation is proposed. In the particular task of grammar detection, researchers have gradually shifted their attention from statistical methods to neural network methods. Three deep learning algorithm models are established, and the multitask performance of the model is better than that of the conditional random field model and the LSTM-CRF model. The reason is that the multitask learning model of auxiliary tasks is included to some extent, which solves the problem of data sparsity and enables the model to be fully trained even under the condition of uneven label distribution. Thus, it performs better than other models in the task of syntax error detection. It realizes the word spelling error check based on the dictionary and uses the thought of editing distance to prompt the word error found, which can automatically check a large number of translations. On the basis of analyzing the sentence structure characteristics of engineering English translation, this paper realizes the detection of subject-verb agreement errors and analyzes the main word of the subject corresponding to the predicate verb by constructing the syntactic structure tree of the sentence, so as to realize the judgment of subject-verb agreement errors.