  Interest in dialog systems has grown substantially in the past decade. By
extension, so too has interest in developing and improving intent
classification and slot-filling models, which are two components that are
commonly used in task-oriented dialog systems. Moreover, good evaluation
benchmarks are important in helping to compare and analyze systems that
incorporate such models. Unfortunately, much of the literature in the field is
limited to analysis of relatively few benchmark datasets. In an effort to
promote more robust analyses of task-oriented dialog systems, we have conducted
a survey of publicly available datasets for the tasks of intent classification
and slot-filling. We catalog the important characteristics of each dataset, and
offer discussion on the applicability, strengths, and weaknesses of each. Our
goal is that this survey aids in increasing the accessibility of these
datasets, which we hope will enable their use in future evaluations of intent
classification and slot-filling models for task-oriented dialog systems.
