  The schema-guided paradigm overcomes scalability issues inherent in building
task-oriented dialogue (TOD) agents with static ontologies. Instead of
operating on dialogue context alone, agents have access to hierarchical schemas
containing task-relevant natural language descriptions. Fine-tuned language
models excel at schema-guided dialogue state tracking (DST) but are sensitive
to the writing style of the schemas. We explore methods for improving the
robustness of DST models. We propose a framework for generating synthetic
schemas which uses tree-based ranking to jointly optimise lexical diversity and
semantic faithfulness. The generalisation of strong baselines is improved when
augmenting their training data with prompts generated by our framework, as
demonstrated by marked improvements in average joint goal accuracy (JGA) and
schema sensitivity (SS) on the SGD-X benchmark.
