A THE Post-editing for the kazakh language

Post-editing for the kazakh language

Abstract

The modern world and our immediate future depend on applied intelligent systems, as new technologies develop every day. One of the tasks of intelligent systems is machine (automated) translation from one natural language to another. Machine translation (MT) allows people to communicate regardless of language differences, as it removes the language barrier and opens up new languages for communication. Machine translation is a new technology, a special step in human development. This type of translation can help when you need to quickly understand what your interlocutor wrote or said in a letter.

The work of online translators used to translate into Kazakh and vice versa. Translation errors are identified, general advantages and disadvantages of online machine translation systems in Kazakh are given. A model for the development of a post-editing machine translation system for the Kazakh language is presented.

OpenNMT (Open Neural Machine Translation) is an open source system for neural machine translation and neural sequence training. To learn languages in OpenNMT, you need parallel corpuses for language pairs. The advantage of OpenNMT is that it can be applied to all languages and can handle large corpora. Experimental data were obtained for the English-Kazakh language pair. Experimental data were obtained for the English-Kazakh language pair.

Applied intelligent systems play an important role in the modern world. One of their tasks is machine translation (MT) from one language into another one. MT allows people to freely communicate despite language barriers. This new technology is a special step in helping to understand what a companion speaks, or writes to you. Automatic post-editing is the task of correcting errors present in texts as a result of machine translation. Since MT cannot always give the desired result, it becomes necessary to edit the translation. The drawbacks of the translation have to be eliminated by post- editing. This need for post-editing is largely determined by the quality of machine translation: low-quality translation leaves a lot of room for post-editing, and high-quality and human translations require minimal text editing. This work describes the development of the light post-editing module for the English-Kazakh language pairs. The neural network model is trained on pairs mt, pe and triplets src, mt, pe using the OpenNMT framework. Then the results of the BLEU metrics mt - pe and mt - ape are compared, and a conclusion about the quality of post-editing is made.