RT Journal Article
T1 Google Translate vs. DeepL: analysing neural machine translation performance under the challenge of phraseological variation.
A1 Hidalgo Ternero, Carlos Manuel
K1 Traducción automática
AB 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.
PB Universidat Jaume I
YR 2020
FD 2020
LK https://hdl.handle.net/10630/33912
UL https://hdl.handle.net/10630/33912
LA eng
NO Hidalgo-Ternero, C. M. (2020). Google Translate vs. DeepL: analysing neural machine translation performance under the challenge of phraseological variation. En P. Mogorrón Huerta (Ed.), Multidisciplinary Analysis of the Phenomenon of Phraseological Variation in Translation and Interpreting. MonTI Special Issue 6, 154-177. https://doi.org/10.6035/MonTI.2020.ne6.5.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026