While this paper has focused on instruction generation, the hierarchical approach in our learning framework helps to scale up to larger NLG tasks, such as text or paragraph generation.
we compare the user satisfaction and naturalness of surface realisation using Hidden Markov Models (HMMs) and Bayesian Networks (BNs) which both have been suggested as generation spaces-spaces of surface form variants for a semantic conceptwithin joint NLG systems (Dethlefs and Cuayáhuitl, 2011a;Dethlefs and Cuayáhuitl, 2011b) and in isolation (Georgila et al., 2002;Mairesse et al., 2010).