Until recently, referring expression generation (reg) research focused on the task of selecting the semantic content of definite mentions of listener-familiar discourse entities. In the grec research programme we have been interested in a version of the reg problem definition that is (i) grounded within discourse context, (ii) embedded within an application context, and (iii) informed by naturally occurring data. This paper provides an overview of our aims and motivations in this research programme, the data resources we have built, and the first three sharedtask challenges, grec-msr'08, grec-msr'09 and grec-neg'09, we have run based on the data. 1 Background Referring Expression Generation (reg) is one of the most lively and thriving subfields of Natural Language Generation (nlg). Traditionally, it has addressed the following question: [G]iven a symbol corresponding to an intended referent, how do we work out the semantic content of a referring expression that uniquely ...