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.
翻译：现有空档填补模型只能从有限的空档中识别预先确定的空档类型。在实际应用中,可靠的对话系统应该知道自己不知道什么。在本文中,我们在以任务为导向的对话系统中引入了一个新的任务,即新式空档探测(NSD ) 。 现有空档填补模型的目的是发现未知或外部空档类型,以加强基于内部培训数据的对话系统的能力。 此外,我们还建立了两个公开的空档,提出了几个强有力的国家空间数据数据库基线,并为未来工作制定基准。 最后,我们进行了详尽的实验和定性分析,以了解关键挑战,并为未来方向提供新的指导。
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