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Elimination of Machine Translation Errors in English Language Transformation

机译：消除英语语言改造的机器翻译错误

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摘要

To improve the level of automation and intelligence of English language transformation in machine translation, a method of machine translation error elimination based on deep learning and feature extraction of language transformation error is proposed. The semantic correlation detection model of error exclusion in English language conversion translation is constructed by using the differentiated semantic modification method, and the semantic tree of error exclusion in English language transformation translation is built by means of grammar analysis. The semantic similarity feature of English language transformation is extracted. According to the different combinations of semantic similarity, the semantic allocation and machine translation error feature analysis in English language transformation are carried out. The tree topic word list of English language conversion is established by means of deep learning method, and the sentence structure of English language transformation is adjusted according to the semantic modification target in the tree topic word list. In order to eliminate the errors in translation of English language conversion and the registration of topic words, the optimal semantic correlation feature of each clause is calculated, and the deep learning algorithm is used to automatically optimize the errors in translation of English language conversion. The simulation results show that the accuracy of the proposed approach is high and the relevance of translation calibration is strong.
机译：提出了一种基于深度学习的机器翻译中英语语言转换的自动化和智能水平，基于深度学习和特征提取语言转换误差的机器翻译错误消除方法。通过使用差异化的语义修改方法构建英语语言转换转换中错误排除的语义关联检测模型，通过语法分析构建了英语语言转换翻译中的错误排除语义树。提取英语语言转换的语义相似性特征。根据语义相似性的不同组合，执行英语语言转换的语义配置和机器翻译错误特征分析。树主题通过深度学习方法建立英语语言转换的单词列表，并且根据树主题Word列表中的语义修改目标调整英语语言转换的句子结构。为了消除英语语言转换的翻译错误和主题词的注册，计算每个子句的最佳语义关联特征，并且深入学习算法用于自动优化英语转换翻译中的错误。仿真结果表明，所提出的方法的准确性很高，翻译校准的相关性很强。

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