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Task-oriented dialog systems empower users to accomplish their goals byfacilitating intuitive and expressive natural language interactions.State-of-the-art approaches in task-oriented dialog systems formulate theproblem as a conditional sequence generation task and fine-tune pre-trainedcausal language models in the supervised setting. This requires labeledtraining data for each new domain or task, and acquiring such data isprohibitively laborious and expensive, thus making it a bottleneck for scalingsystems to a wide range of domains. To overcome this challenge, we introduce anovel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD,that leverages domain schemas to allow for robust generalization to unseendomains and exploits effective summarization of the dialog history. We employGPT-2 as a backbone model and introduce a two-step training process where thegoal of the first step is to learn the general structure of the dialog data andthe second step optimizes the response generation as well as intermediateoutputs, such as dialog state and system actions. As opposed tostate-of-the-art systems that are trained to fulfill certain intents in thegiven domains and memorize task-specific conversational patterns, ZS-ToD learnsgeneric task-completion skills by comprehending domain semantics via domainschemas and generalizing to unseen domains seamlessly. We conduct an extensiveexperimental evaluation on SGD and SGD-X datasets that span up to 20 uniquedomains and ZS-ToD outperforms state-of-the-art systems on key metrics, with animprovement of +17% on joint goal accuracy and +5 on inform. Additionally, wepresent a detailed ablation study to demonstrate the effectiveness of theproposed components and training mechanism

Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema