Efficiency of Machine Translation in Urban Discourse

Abstract

This article aims to analyze the use of Yandex.Translate, an online machine translation system, in translating urban discourse texts on the web. The authors use integrative linguistic-and-pragmatic approach to assess machine translation quality in a global digital setting. The aim is to show the efficiency of a state-of-the-art machine translation system and to investigate its usefulness in practical application. The authors perform a detailed analysis of the Paris city website content, which is automatically translated from French into Russian with Yandex.Translate. The data selection is justified by the absence of official foreign versions of this website, which points to the need of machine translation engines integrated in a web browser. Less than 20% of the analysed machine-translated texts demonstrate high language quality, whereas 60% can be referred to as acceptable – the text preserves the meaning of the source but contains some errors and inaccuracies in the target language. About 20% of the machine-translated text contains blunders, which violate Russian language norms. It causes source text contents distortion and communication failures. In the end, a classification of the system errors is presented. It is also concluded that machine translation would substitute middle-skilled human translators in the future. However, the use of such systems will enforce standardisation and simplification of the target language.