14 citations · 15 across the 3 of their papers we have counts for
4 papers
Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing
Antonio Castaldo, Sheila Castilho, Joss Moorkens +1
Post-editing machine translation (MT) for creative texts, such as literature, requires balancing efficiency with the preservation of creativity and style. While neural MT systems s…
Training in translation tools and technologies: Findings of the EMT survey 2023
Andrew Rothwell, Joss Moorkens, Tomas Svoboda
This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was…
Sociotechnical Effects of Machine Translation
Joss Moorkens, Andy Way, Séamus Lankford
While the previous chapters have shown how machine translation (MT) can be useful, in this chapter we discuss some of the side-effects and risks that are associated, and how they m…
Towards transparency in NLP shared tasks
Carla Parra Escartín, Teresa Lynn, Joss Moorkens +1
This article reports on a survey carried out across the Natural Language Processing (NLP) community. The survey aimed to capture the opinions of the research community on issues su…