While current NMT and GPT models improve fluency and context awareness, they struggle with creative texts, where figurative language and stylistic choices are crucial. Current evaluation methods fail to capture these nuances, which requires a more descriptive approach. We propose a taxonomy based on translation techniques to assess machine-generated translations more comprehensively. The pilot study we conducted comparing human machine-produced translations reveals that human translations employ a wider range of techniques, enhancing naturalness and cultural adaptation. NMT and GPT models, even with prompting, tend to simplify content and introduce accuracy errors. Our findings highlight the need for refined frameworks that consider stylistic and contextual accuracy, ultimately bridging the gap between human and machine translation performance.

Neural Machine Translation intensifies educational challenges in translation technologies. The MultiTraiNMT project developed MutNMT, an open-source, didactic platform for training and evaluating NMT systems. Building upon it, LT-LiDER introduces ProMut which implements three main novel features: migration of the core NMT framework from JoeyNMT to MarianNMT, close integration with OPUS datasets, engines and connectors and the addition of a researcher profile for larger datasets and extended training processes and evaluation.

LT-LiDER is an Erasmus+ cooperation project with two main aims. The first is to map the landscape of technological capabilities required to work as a language and/or translation expert in the digitalised and datafied language industry. The second is to generate training outputs that will help language and translation trainers improve their skills and adopt appropriate pedagogical approaches and strategies for integrating data-driven technology into their language or translation classrooms, with a focus on digital and AI literacy.

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.

The MultitraiNMT Erasmus+ project has developed an open innovative syl-labus in machine translation, focusing on neural machine translation (NMT) and targeting both language learners and translators. The training materials include an open access coursebook with more than 250 activities and a pedagogical NMT interface called MutNMT that allows users to learn how neural machine translation works. These materials will allow students to develop the technical and ethical skills and competences required to become informed, critical users of machine translation in their own language learn-ing and translation practice. The pro-ject started in July 2019 and it will end in July 2022.

The MultiTraiNMT Erasmus+ project aims at developing an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource that can support citizens in their attempt to acquire and develop language skills if they are trained in an informed and critical way. Machine translation could thus help tackle the mismatch between the desired EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which citizens generally fall far short of this objective. The training materials consists of an open-access coursebook, an open-source NMT web application called MutNMT for training purposes, and corresponding activities.

This paper reports the results of two studies carried out with two different group of professional translators to find out how professionals perceive and accept SMT in comparison with TM. The first group translated and post-edited segments from English into German, and the second group from English into Spanish. Both studies had equivalent settings in order to guarantee the comparability of the results. It will also help to shed light upon the real benefit of SMT from which translators may take advantage.

According to Torres Hostench et al. (2016), the use of machine translation (MT) in Catalan and Spanish translation companies is low. Based on these results, the Tradumàtica research group,2 through the ProjecTA and ProjecTA-U projects,3 set to bring MT and translators closer with a two-fold strategy. On the one hand, by developing MTradumàtica, a free Moses-based web platform with graphical user interface (GUI) for statistical machine translation (SMT) trainers. On the other hand, by including MT-related contents in translators’ training. This paper will describe the latest developments in MTradumàtica.

The aim of this study is to analyse whether translation trainees who are not native speakers of the target language are able to perform as well as those who are native speakers, and whether they achieve the expected quality in a “good enough” post-editing (PE) job. In particular the study focuses on the performance of two groups of students doing PE from Spanish into English: native English speakers and native Spanish speakers. A pilot study was set up to collect evidence to compare and contrast the two groups’ performances. Trainees from both groups had been given the same training in PE and were asked to post-edit 30 sentences translated from Spanish to English. The PE output was analyzed taking into account accuracy errors (mistranslations and omissions) as well as language errors (grammatical errors and syntax errors). The results show that some native Spanish speakers corrected just as many errors as the native English speakers. Furthermore, the Spanish-speaking trainees outperformed their English-speaking counterparts when identifying mistranslations and omissions. Moreover, the performances of the best English-speaking and Spanish-speaking trainees at identifying grammar and syntax errors were very similar.