  Task-oriented dialog systems empower users to accomplish their goals by
facilitating intuitive and expressive natural language interactions.
State-of-the-art approaches in task-oriented dialog systems formulate the
problem as a conditional sequence generation task and fine-tune pre-trained
causal language models in the supervised setting. This requires labeled
training data for each new domain or task, and acquiring such data is
prohibitively laborious and expensive, thus making it a bottleneck for scaling
systems to a wide range of domains. To overcome this challenge, we introduce a
novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD,
that leverages domain schemas to allow for robust generalization to unseen
domains and exploits effective summarization of the dialog history. We employ
GPT-2 as a backbone model and introduce a two-step training process where the
goal of the first step is to learn the general structure of the dialog data and
the second step optimizes the response generation as well as intermediate
outputs, such as dialog state and system actions. As opposed to
state-of-the-art systems that are trained to fulfill certain intents in the
given domains and memorize task-specific conversational patterns, ZS-ToD learns
generic task-completion skills by comprehending domain semantics via domain
schemas and generalizing to unseen domains seamlessly. We conduct an extensive
experimental evaluation on SGD and SGD-X datasets that span up to 20 unique
domains and ZS-ToD outperforms state-of-the-art systems on key metrics, with an
improvement of +17% on joint goal accuracy and +5 on inform. Additionally, we
present a detailed ablation study to demonstrate the effectiveness of the
proposed components and training mechanism
