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.

As post-editing of machine translation (PEMT) is becoming one of the most dominant services offered by the language services industry (LSI), efforts are being made to support provision of this service with additional technology. We present text-to-speech (T2S) as a potential attention-raising technology for post-editors. Our study was conducted with university students and included both PEMT and error annotation of a creative text with and without T2S. Focusing on the error annotation data, our analysis finds that participants under-annotated fewer MT errors in the T2S condition compared to the silent condition. At the same time, more over-annotation was recorded. Finally, annotation performance corresponds to participants’ attitudes towards using T2S.

In an age when demand for innovative and motivating language teaching methodologies is at a very high level, TREAT - the Trilingual REAding Tutor - combines the most advanced natural language processing (NLP) techniques with the latest second and third language acquisition (SLA/TLA) research in an intuitive and user-friendly environment that has been proven to help adult learners (native speakers of L1) acquire reading skills in an unknown L3 which is related to (cognate with) an L2 they know to some extent. This corpus-based methodology relies on existing linguistic resources, as well as materials that are easy to assemble, and can be adapted to support other pairs of L2-L3 related languages, as well. A small evaluation study conducted at the Leeds University Centre for Translation Studies indicates that, when using TREAT, learners feel more motivated to study an unknown L3, acquire significant linguistic knowledge of both the L3 and L2 rapidly, and increase their performance when translating from L3 into L1.