Machine translation method and device and storage medium

Abstract

The invention relates to a machine translation method and device and a storage medium. The method comprises the steps of inputting M source language sentences with the same semanteme and different source languages into corresponding coding layers in a translation model respectively to obtain M original coding results, wherein M is a positive integer; associating words of different source languageswith the same semanteme in the M source language sentences by utilizing a word alignment model to obtain an alignment result; inputting the M original coding results into an interaction layer of a translation model, and obtaining M target coding results according to the alignment result and the M original coding results; and based on the M target coding results, obtaining a target sentence basedon target language representation. According to the method, the information transmission among all source language sentences is greatly enhanced, so that the source end input information is more fullyutilized, and the accuracy of a translation result is improved; and semantic interaction between sentences is guided by utilizing an alignment result, so that the information interaction efficiency is improved, and meanwhile, the training burden of the model is also reduced.

Description

technical field [0001] The present disclosure relates to the field of machine translation, in particular to a machine translation method, device and storage medium. Background technique [0002] Machine translation is the process of using computers to automatically translate one language into another. It is one of the frontier research directions of artificial intelligence natural language processing and has important academic significance and application value. With the rapid development of economic globalization and the Internet, machine translation technology plays an increasingly important role in promoting political, economic, and cultural exchanges, and has attracted people's attention. [0003] In real life, there are many application scenarios of multi-source machine translation. For example, retranslating a book into a new language that has translations in different languages. Another example is the translation of multilingual tool manuals, multilingual web pages,...

AI Technical Summary

Problems solved by technology

At present, when using machine translation models to model source sentences, different languages ​​are modeled separately, and the relationship between sentences in different source languages ​​cannot be fully utilized, even if multiple source language sentences are combined into one sentence , because this approach is too simple, it is difficult to fully and effectively interact between the source language sentences, which will lead to low translation efficiency and inaccurate translation results

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