Using Machine Learning Techniques to Improve the Accuracy of Computer Translation in the English Translation of Specialized Terms

At present, machine translation performs better in the general domain translation effect of large-scale bilingual corpus, but the translation effect in specific domains still needs to be improved.In order to optimize the accuracy of machine translation in the domain of English translation of professional terms, this paper proposes a translation model that incorporates syntactic knowledge and terminology.Aiming at the problem of more limited translation domain knowledge in the RNMT and Transformer models based on the self-attention mechanism, an optimization method is proposed.According to the domain characteristics of English translation of professional terms, English syntactic keywords are incorporated into the model training process, the special information contained inside the text of professional terms is learned, and the lexical properties of each word in the dataset are recognized before they are input into the model.Then attempts are made to incorporate the specialized terminology into the model to enrich the parallel corpus required by the model.The experiments confirm the excellent performance of the optimized translation model in this paper on the DeEn terminology translation task, which improves 22.67 BLEU values compared to the base model.And the fluctuation of its BLEU value with the change of sentence length is small, which further indicates that the method optimizes the accuracy of the machine translation model in the English translation of professional terms.