QT21 is a major European Machine Translation (MT) research and innovation initiative including 11 universities and research institutions (DFKI, FBK, RWTH, University of Amsterdam, DCU, University of Edinburgh, KIT, CNRS, Charles University, HKUST, and University of Sheffield) and 3 industry partners (TAUS, text&form, TILDE). The project aims to develop (1) substantially improved statistical and machine-learning based translation models for challenging languages and resource scenarios, (2) improved evaluation and continuous learning from mistakes, guided by a systematic analysis of quality barriers, informed by human translators, (3) all with a strong focus on scalability, to ensure that learning and decoding with these models is efficient and that reliance on data (annotated or not) is minimized. To continuously measure progress, and to provide a platform for sharing and collaboration (QT21 internally and beyond), the project revolves around a series of Shared Tasks, for maximum impact co-organised with WMT. To support early technology transfer, QT21 proposes a Technology Bridge linking ICT-17(a) and (b) projects and opening up the possibility of showing the technical feasibility of early research outputs in near to operational environments.
Funding: Horizon 2020 – RIA