Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.

Current work in surface realization concentrates on the use of general, abstract algorithms that interpret large, reversible grammars.Only little attention has been paid so far to the many small and simple applications that require coverage of a small sublanguage at different degrees of sophistication.The system TG/2 described in this paper can be smoothly integrated with deep generation processes, it integrates canned text, templates, and context-free rules into a single formalism, it allows for both textual and tabular output, and it can be parameterized according to linguistic preferences.These features are based on suitably restricted production system techniques and on a generic backtracking regime. MotivationCurrent work in surface realization concentrates on the use of general, abstract algorithms that interpret declaratively defined, non-directional grammars.It is claimed that this way, a grammar can be reused for parsing *This work has been supported by a