WiKollect aims at creating a workflow for the large-scale transference of high-quality contents across languages. The workflow is divided in four cyclic steps. In step (i) an automatic model will identify contents available in a document in language A which are missing in a document, on the same topic, in language B. In step (ii) candidates to fill the gaps in the document in language B will be automatically generated. In step (iii) such candidates will be subject to manual evaluation by language learners. In step (iv) the contents identified as high-quality will be promoted to fill the gaps in the document in language B. WiKollect will take advantage of the barely-exploited synergy among natural language processing, language learning, and crowdsourcing. To address the different research challenges posed by the workflow design and implementation, it will create an innovative and re-usable hybrid intelligence architecture combining (a) artificial intelligence —such as machine learning and natural language processing— to identify contents worth transferring across languages and generate potential translations and (b) human intelligence —by means of implicit crowdsourcing— relying on a crowd of language learners to flag good contents. WiKollect will create different by-products in addition to the research products that will be generated by addressing each step in the four-step workflow. Language learning exercises on specific topics and complexity levels will be generated. The fair re-use of contents across languages will be promoted with the mass production of high-quality contents. During the MSC period, WiKollect will target the generation of Wiktionary contents in Italian and German. Still, the workflow is flexible and extendable and can be applied to other documents (e.g., Wikipedia articles, news) and languages in the near future.