Abstract
At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness. This paper proposes a framework that integrates vocabulary alignment structure for neural machine translation at the vocabulary level. Under the proposed framework, the neural machine translation decoder receives external vocabulary alignment information during each step of the decoding process to further alleviate the problem of missing vocabulary alignment structure. Specifically, this article uses the word alignment structure of statistical machine translation as the external vocabulary alignment information and introduces it into the decoding step of neural machine translation. The model is mainly based on neural machine translation, and the statistical machine translation vocabulary alignment structure is integrated on the basis of neural networks and continuous expression of words. In the model decoding stage, the statistical machine translation system provides appropriate vocabulary alignment information based on the decoding information of the neural machine translation and recommends vocabulary based on the vocabulary alignment information to guide the neural machine translation decoder to more accurately estimate its vocabulary in the target language. From the aspects of data processing methods and machine translation technology, experiments are carried out to compare the data processing methods based on language model and sentence similarity and the effectiveness of machine translation models based on fusion principles. Comparative experiment results show that the data processing method based on language model and sentence similarity effectively guarantees data quality and indirectly improves the algorithm performance of machine translation model; the translation effect of neural machine translation model integrated with statistical machine translation vocabulary alignment structure is compared with other models.
Highlights
Machine translation refers to the process of converting two natural languages through a computer on the premise of keeping the semantics unchanged, involving knowledge in many fields including linguistics, computer science, and mathematics [1]
Experimental results show that data processing methods based on language models and sentence similarity can improve data quality to a certain extent, and neural machine translation models incorporating statistical machine translation vocabulary alignment structures can effectively improve machine translation results
With the development of neural networks, the birth of neural machine translation technology has opened up a new path in the field of machine translation [4]. e emergence of neural machine translation technology has solved many of the abovementioned shortcomings of statistical machine translation
Summary
Machine translation refers to the process of converting two natural languages through a computer on the premise of keeping the semantics unchanged, involving knowledge in many fields including linguistics, computer science, and mathematics [1]. E translation quality of machine translation is slightly inferior to manual translation, but relying on the powerful computing power of computers and the rapid development of the Internet has greatly increased the speed of translation and reduced costs. It has been used by many companies in largescale translation scenarios. Experimental results show that data processing methods based on language models and sentence similarity can improve data quality to a certain extent, and neural machine translation models incorporating statistical machine translation vocabulary alignment structures can effectively improve machine translation results
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