  Natural language generation provides designers with methods for automatically
generating text, e.g. for creating summaries, chatbots and game content. In
practise, text generators are often either learned and hard to interpret, or
created by hand using techniques such as grammars and templates. In this paper,
we introduce a novel grammar induction algorithm for learning interpretable
grammars for generative purposes, called Gitta. We also introduce the novel
notion of template trees to discover latent templates in corpora to derive
these generative grammars. By using existing human-created grammars, we found
that the algorithm can reasonably approximate these grammars using only a few
examples. These results indicate that Gitta could be used to automatically
learn interpretable and easily modifiable grammars, and thus provide a stepping
stone for human-machine co-creation of generative models.
