  Neural natural language generation (NNLG) systems are known for their
pathological outputs, i.e. generating text which is unrelated to the input
specification. In this paper, we show the impact of semantic noise on
state-of-the-art NNLG models which implement different semantic control
mechanisms. We find that cleaned data can improve semantic correctness by up to
97%, while maintaining fluency. We also find that the most common error is
omitting information, rather than hallucination.
