The field of Machine Translation has made significant progress in recent years, and its impact on the Language Services Industry continues to attract the attention of academics and professionals alike. However, while industry workflows have been changing and translators have had to adapt to new tasks and requirements, their computer-assisted translation (CAT) have not changed much. Our latest research project investigates a new way of working with machine translation output, designed to help mitigate the “Fluency trap” (the false sense of security given by the more fluent neural machine translation hypotheses which are not necessarily more accurate, too). In our project, professional translators worked in a CAT tool enhanced with automatic speech synthesis functionalities and completed machine translation post-editing tasks in silence, as well as with the benefit of hearing an artificial voice “speaking” the source and target text. This session will share initial findings regarding their productivity, cognitive load, and also the quality of their output.
About the Speaker:
Dragoș Ciobanu is Professor of Computational Terminology and Machine Translation in the University of Vienna Centre for Translation Studies. He leads the HAITrans research group (Human and Artificial Intelligence in Translation – https://haitrans.univie.ac.at/) and investigates ways to improve localization workflows by integrating translation and speech technologies, as well as methods to optimise collaborative translation and training practices. He collaborates with Language Service Providers from around the world and trains linguists from International Organisations to maximize the use of language, localisation, and project management technologies.
HAITrans Research Group website