he Erasmus+-funded international research consortium LT-LiDER develops a range of digital training resources which are grounded in the overarching frameworks of digital and AI literacy and oriented towards practical application contexts in the language and translation industry. These resources can be implemented on a component basis or as a complete curriculum in higher-education language and translation classrooms.

The aim of this article is to present a new Neural Machine Translation (NMT) from Spanish into Galician for the social media domain that was trained with a Twitter corpus. Our main goal is to outline the methods used to build the corpus and the steps taken to train the engine in a low-resource language context. We have evalu-ated the engine performance both with regular automatic metrics and with a new methodology based on the non-inferiority process and contrasted this information with an error classification human evalua-tion conducted by professional linguists. We will present the steps carried out fol-lowing the conclusions of a previous pilot study, describe the new process followed, analyze the new engine and present the final conclusions.