Automatic speech synthesis has seen rapid development and integration in domains as diverse as accessibility services, translation, or language learning platforms. We analyse its integration in a post-editing machine translation (PEMT) environment and the effect this has on quality, productivity, and cognitive effort. We use Bayesian hierarchical modelling to analyse eye-tracking, time-tracking, and error annotation data resulting from an experiment involving 21 professional translators post-editing from English into German in a customised cloud-based CAT environment and listening to the source and/or target texts via speech synthesis. Using speech synthesis in a PEMT task has a non-substantial positive effect on quality, a substantial negative effect on productivity, and a substantial negative effect on the cognitive effort expended on the target text, signifying that participants need to allocate less cognitive effort to the target text.

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.

Multilingual Neural Machine Translation (MNMT) models allow to translate across multiple languages based on only one system. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation based on the Multidimensional Quality Metrics (MQM). We further expand the MQM typology to include terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT on a standard test dataset of abstracts from medical publications. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer errors. In addition, we perform the manual annotation over the reference test dataset to study the quality of the reference translations. We identify a high number of omissions, additions, and mistranslations in the reference dataset, and comment on the assumed accuracy of existing datasets. Finally, we compare the correlation between the COMET, BERTScore, and chrF automatic metrics with the MQM annotated translations. COMET shows a better correlation with the MQM scores compared to the other metrics.