A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog

Indeed, commercial task-oriented dialog systems in the form of smart devices like Amazon's Alexa are used by millions of people every day. Within the academic research community, however, task-oriented dialog system models are often benchmarked on relatively few evaluation datasets. This is in spite of the fact that the past few years have seen a substantial growth in the number of available datasets for building and evaluating intent classification and slot-filling models for task-oriented dialog systems. Thus, the goal of this survey is to catalog these intent classification and slot-filling datasets to help facilitate their use in building and evaluating dialog systems and beyond. Other surveys have discussed dialog datasets in depth (Serban et al. 2018), but exclude almost all intent classification and slot-filling datasets, and model-focused surveys on dialog systems mostly focus on models and pay much less attention to datasets.