  Zero/few-shot transfer to unseen services is a critical challenge in
task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset
introduced a paradigm for enabling models to support any service in zero-shot
through schemas, which describe service APIs to models in natural language. We
explore the robustness of dialogue systems to linguistic variations in schemas
by designing SGD-X - a benchmark extending SGD with semantically similar yet
stylistically diverse variants for every schema. We observe that two top state
tracking models fail to generalize well across schema variants, measured by
joint goal accuracy and a novel metric for measuring schema sensitivity.
Additionally, we present a simple model-agnostic data augmentation method to
improve schema robustness.
