  Developing mechanisms that flexibly adapt dialog systems to unseen tasks and
domains is a major challenge in dialog research. Neural models implicitly
memorize task-specific dialog policies from the training data. We posit that
this implicit memorization has precluded zero-shot transfer learning. To this
end, we leverage the schema-guided paradigm, wherein the task-specific dialog
policy is explicitly provided to the model. We introduce the Schema Attention
Model (SAM) and improved schema representations for the STAR corpus. SAM
obtains significant improvement in zero-shot settings, with a +22 F1 score
improvement over prior work. These results validate the feasibility of
zero-shot generalizability in dialog. Ablation experiments are also presented
to demonstrate the efficacy of SAM.
