  Neural network based approaches to data-to-text natural language generation
(NLG) have gained popularity in recent years, with the goal of generating a
natural language prompt that accurately realizes an input meaning
representation. To facilitate the training of neural network models,
researchers created large datasets of paired utterances and their meaning
representations. However, the creation of such datasets is an arduous task and
they mostly consist of simple meaning representations composed of slot and
value tokens to be realized. These representations do not include any
contextual information that an NLG system can use when trying to generalize,
such as domain information and descriptions of slots and values. In this paper,
we present the novel task of Schema-Guided Natural Language Generation
(SG-NLG). Here, the goal is still to generate a natural language prompt, but in
SG-NLG, the input MRs are paired with rich schemata providing contextual
information. To generate a dataset for SG-NLG we re-purpose an existing dataset
for another task: dialog state tracking, which includes a large and rich schema
spanning multiple different attributes, including information about the domain,
user intent, and slot descriptions. We train different state-of-the-art models
for neural natural language generation on this dataset and show that in many
cases, including rich schema information allows our models to produce higher
quality outputs both in terms of semantics and diversity. We also conduct
experiments comparing model performance on seen versus unseen domains, and
present a human evaluation demonstrating high ratings for overall output
quality.
