Modern Machine Translation Systems: Trends and Prospects

The modern world is moving towards global digitalization and accelerated software development with a clear tendency to replace human resources by digital services or programs that imitate the doing of similar tasks. There is no doubt that, long term, the use of such technologies has economic benefits for enterprises and companies. Despite this, however, the quality of the final result is often less than satisfactory, and machine translation systems are no exception, as editing of texts translated by using online translation services is still a demanding task. At the moment, producing high-quality translations using only machine translation systems remains impossible for multiple reasons, the main of which lies in the mysteries of natural language: the existence of sublanguages, abstract words, polysemy, etc. Since improving the quality of machine translation systems is one of the priorities of natural language processing (NLP), this article describes current trends in developing modern machine translation systems as well as the latest advances in the field of natural language processing (NLP) and gives suggestions about software innovations that would minimize the number of errors. Even though recent years have seen a significant breakthrough in the speed of information analysis, in all probability, this will not be a priority issue in the future. The main criteria for evaluating the quality of translated texts will be the semantic coherence of these texts and the semantic accuracy of the lexical material used. To improve machine translation systems, we should introduce elements of data differentiation and personalization of information for individual users and their tasks, employing the method of thematic modeling for determining the subject area of a particular text. Currently, there are algorithms based on deep learning that are able to perform these tasks. However, the process of identifying unique lexical units requires a more detailed linguistic description of their semantic features. The parsing methods that will be used in analyzing texts should also provide for the possibility of clustering by sublanguages. Creating automated electronic dictionaries for specific fields of professional knowledge will help improve the quality of machine translation systems. Notably, to date there have been no successful projects of creating dictionaries for machine translation systems for specific sub-languages. Thus, there is a need to develop such dictionaries and to integrate them into existing online translation systems.