  Task-oriented dialog (TOD) systems play a crucial role in facilitating
efficient interactions between users and machines by focusing on achieving
specific goals through natural language communication. These systems
traditionally rely on manually annotated metadata, such as dialog states and
policy annotations, which is labor-intensive, expensive, inconsistent, and
prone to errors, thereby limiting the potential to leverage the vast amounts of
available conversational data. A critical aspect of TOD systems involves
accessing and integrating information from external sources to effectively
engage users. The process of determining when and how to query external
resources represents a fundamental challenge in system design, however existing
approaches expect this information to provided in the context. In this paper,
we introduce Natural Language Task Oriented Dialog System (NL-ToD), a novel
model that removes the dependency on manually annotated turn-wise data by
utilizing dialog history and domain schemas to create a Zero Shot Generalizable
TOD system. We also incorporate query generation as a core task of the system,
where the output of the system could be a response to the user or an API query
to communicate with an external resource. To achieve a more granular analysis
of the system output, we classify the output into multiple categories: slot
filling, retrieval, and query generation. Our analysis reveals that slot
filling is the most challenging TOD task for all models. Experimental results
on three popular TOD datasets (SGD, KETOD and BiToD) shows the effectiveness of
our approach as NL-ToD outperforms state-of-the-art approaches, particularly
with a \textbf{31.4\%} and \textbf{82.1\%} improvement in the BLEU-4 score on
the SGD and KETOD dataset.
