Google Translate vs. DeepL: analysing neural machine translation performance under the challenge of phraseological variation.

Abstract

The present research analyses the performance of two free open-source neural machine translation (NMT) systems —Google Translate and DeepL— in the (ES>EN) translation of somatisms such as tomar el pelo and meter la pata, their nominal variants (tomadura/tomada de pelo and metedura/metida de pata), and other lower-frequency variants such as meter la pata hasta el corvejón, meter la gamba and metedura/metida de gamba. The machine translation outcomes will be contrasted and classified depending on whether these idioms are presented in their continuous or discontinuous form (Anastasiou 2010), i.e., whether different n-grams split the idiomatic sequence (or not), which may pose some difficulties for their automatic detection and translation. Overall, the insights gained from this study will prove useful in determining for which of the different scenarios either Google Translate or DeepL delivers a better performance under the challenge of phraseological variation and discontinuity.