Pre-editing English news texts for machine translation into Russian

Abstract

The paper discusses the possible advantages of pre-editing English news texts for machine translation into Russian. Pre-editing is defined as a process of adapting source text in order to reach a better quality of machine translation. Two case studies were conducted: one in 2021 and the other one in 2022. During case studies texts from bbc.com were chosen, pre-edited and translated using neural machine translation systems. Analysing common pre-editing techniques and their impact on the result of machine translation in terms of certain error patterns we conclude that in most cases pre-editing helps to eliminate a number of errors, improve the overall quality of machine output and reduce the amount of time and efforts needed for post-editing machine translation. The conducted case study also showed that despite the fact, that machine translation systems are constantly developing and changing, it is possible to identify common peculiarities of machine translation regarding a certain style or type of text and certain language pair, analyse the error patterns and find the appropriate pre-editing techniques that will be applicable for the most of machine translation systems for many months and years. Pre-editing does not guarantee the high quality of the translation itself, but together with post-editing it allows reaching results similar or equal to human translation saving a translator’s time and efforts.