  Existing slot filling models can only recognize pre-defined in-domain slot
types from a limited slot set. In the practical application, a reliable
dialogue system should know what it does not know. In this paper, we introduce
a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system.
NSD aims to discover unknown or out-of-domain slot types to strengthen the
capability of a dialogue system based on in-domain training data. Besides, we
construct two public NSD datasets, propose several strong NSD baselines, and
establish a benchmark for future work. Finally, we conduct exhaustive
experiments and qualitative analysis to comprehend key challenges and provide
new guidance for future directions.
