  We present the first dataset targeted at end-to-end NLG in Czech in the
restaurant domain, along with several strong baseline models using the
sequence-to-sequence approach. While non-English NLG is under-explored in
general, Czech, as a morphologically rich language, makes the task even harder:
Since Czech requires inflecting named entities, delexicalization or copy
mechanisms do not work out-of-the-box and lexicalizing the generated outputs is
non-trivial.
  In our experiments, we present two different approaches to this this problem:
(1) using a neural language model to select the correct inflected form while
lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model
generates an interleaved sequence of lemmas and morphological tags, which are
then inflected by a morphological generator.

Computation and Language