Abstract

The article is devoted to the study of the impact of using the neural machine translation system Google Translate on the quality of translation of texts in the field of pharmacognosy. At the present stage, the work of a translator is impossible to imagine without the use of information and communication technologies, an important place among which is attributed to machine translation. It is considered that neural machine translation systems perform translation at a fairly high level, so that its use by a human translator can have a positive impact. That is why the aim of the study was to conduct an experiment to determine the impact of using a neural machine translation system on the quality of translation of texts in the field of pharmacognosy in terms of the number of errors and correctness of translating terminology. The article formulates a research hypothesis, describes the text chosen to conduct the study and the neural machine translation system, which was selected for this purpose, discloses the procedure for estimating the number of errors in translations and calculating the percentage of correctness of translating terminology, provides quantitative experimental data, and the results are illustrated in tables and drawings. The experimental study was conducted in the first semester of the 2020/2021 academic year (September) on the basis of an excerpt from a text in the field of pharmacognosy, which was translated by the neural machine translation system Google Translate and a translation student of the bachelor’s level. Both translations were checked in terms of quantity and quality (types) of errors, as well as in terms of correctness of translating domain-specific terminology. The results refuted our hypothesis, as the translation performed by the neural machine translation system Google Translate was worse, both in terms of the number of errors and the percentage of correctness of translating terminology as compared to the results demonstrated by the student.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call