Challenging machine translation engines: some Spanish-English linguistic problems put to the test

Abstract

This work is an evaluation of machine translation engines completed in 2018 and 2021, inspired by Isabelle, Cherry & Foster (2017), and Isabelle & Kuhn (2018). The challenge consisted of testing MTs Google Translate and Bing and DeepL in the translation of certain linguistic problems normally found when translating from Spanish into English. The divergences representing a “challenge” to the engines were of morphological and lexical-syntactical types. The absolute winner of the challenge was DeepL, in second place was Bing from Microsoft, and Google was the engine that was the poorest in the management of the linguistic problems. In terms of time, when comparing the engines three years apart, it was found that DeepL was the only one that enhanced its performance by correcting a problem it had before in a test sentence. This was not the case for the other two, on the contrary, their translations were of lower quality. These machines do not seem to be consistent in the manner in which they are improved. These findings may be valuable for translators who may work with these systems as pre or post-editors so that their efforts may be better directed.

Exploring cognitive processes in specialized text post-editing: a mixed methods approach integrating eye-tracking and verbalization protocols

“…Despite technological advancements facilitating the translation process to some extent, it remains evident that MT outputs often lack cultural and contextual suitability for the target language and culture. Hence, there persists a crucial need for human involvement, whether in the capacity of translators or posteditors to ensure the overall adequacy of the whole process (Peña, 2023).…”