Recent advances in corpus-based NLG (Dušek and Jurčíček, 2015; <cite>Wen et al., 2015</cite>; Mei et al., 2016; Wen et al., 2016; Dušek and Jurčíček, 2016; Lampouras and Vlachos, 2016) require costly training data, consisting of meaning representations (MRs) paired with corresponding NL texts. In our work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating two types of MRs, pictorial and textual, used to elicit data (Novikova and Rieser, 2016) .