Automatic translation is an undeniable need in a globalized world where communication using several languages becomes increasingly more relevant. Translation Memory (TM) and Machine Translation (MT) systems are the two most elaborate technologies to support human translation. Recent developments in the area of Example-based and Statistical Machine Translation (EBMT and SMT), in particular, have shown the potential of data-driven approaches for producing fast and low cost translations. A number of user studies have however established shortcomings in the state-of-the-art of these technologies, including poor quality translations for low resource languages, interfaces that do not take into account user requirements and user feedback, etc. We propose the creation of an Initial Training Network to train young researchers on ways to improve current data-driven MT technologies (TM, SMT and EBMT) by exploiting their individual strengths through their combination and by addressing some of the main limitations of each of these technologies. Leading academic and industrial partners in all data-driven translation technologies, along with both professional translators and end-users of translation technologies will support young researchers of the network during the whole research and development cycle, providing guidance, core and complementary training skills and evaluating the resulting technologies. A comprehensive set of training materials on core and complementary skills developed during this project will be made freely available to other researchers interested in the field. We expect the training of researchers in the new skills required for the development and use of technologies that can increase productivity and reduce costs in the translation sector, as well as facilitate reliable communication and content creation in multiple languages, will contribute to several aspects of Europe’s ICT development.