The emergence of neural methods changed this: provided there is enough training data, it does become possible to learn a direct mapping from input to output, as has also been shown in, for example, neural machine translation.
in NLG more recently, neural end-to-end data-totext models have been proposed, which directly learn input-output mappings and rely much less on explicit intermediate representations (Wen et al., 2015;Dušek and Jurcicek, 2016;Mei et al., 2016;Lebret et al., 2016;Gehrmann et al., 2018).