Монгол-Aнгли орчуулга хийдэг нейрон сүлжээнд суурилсан машины орчуулгын гурван шатлалт загвар боловсруулах

Abstract

The widespread use of neural machine translation has the advantage of allowing users to translate terms and translate untrained data to a certain extent, but in some cases often results in distorted sentence structure. This study aims to address issues such as neural machine translation control, high-probability translation of unrecognized data, correct sentence structure, beginning and ending recognition, and the establishment of an independent, machine translator in one's home country. We have made improvements to the neural network model, such as adjusting neural machine translation to unidentified words in subunits, and defining sentence boundaries and scope. The design is based on the usual PMT and SMT templates used to compare words in a system that takes into account word and sentence structure. However, the model we developed is based on the latest neural machine translation (NMT) architecture, which can make more complex relationships. In this sense, this work can be seen as an attempt to use a combination of statistical machine translation and neural machine translation. We sought and tested in practice a step-by-step approach to combining complex deep neural network models that included longer contexts in a system that considered only short contexts in terms of word and sentence structures.