  Natural language generators (NLGs) for task-oriented dialogue typically take
a meaning representation (MR) as input. They are trained end-to-end with a
corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue
acts and domain attributes. Creation of such datasets is labor-intensive and
time-consuming. Therefore, dialogue systems for new domain ontologies would
benefit from using data for pre-existing ontologies. Here we explore, for the
first time, whether it is possible to train an NLG for a new larger ontology
using existing training sets for the restaurant domain, where each set is based
on a different ontology. We create a new, larger combined ontology, and then
train an NLG to produce utterances covering it. For example, if one dataset has
attributes for family-friendly and rating information, and the other has
attributes for decor and service, our aim is an NLG for the combined ontology
that can produce utterances that realize values for family-friendly, rating,
decor and service. Initial experiments with a baseline neural
sequence-to-sequence model show that this task is surprisingly challenging. We
then develop a novel self-training method that identifies (errorful) model
outputs, automatically constructs a corrected MR input to form a new (MR,
utterance) training pair, and then repeatedly adds these new instances back
into the training data. We then test the resulting model on a new test set. The
result is a self-trained model whose performance is an absolute 75.4%
improvement over the baseline model. We also report a human qualitative
evaluation of the final model showing that it achieves high naturalness,
semantic coherence and grammaticality
