Neural dialog models often lack robustness to anomalous user\u00a0input and produce inappropriate responses which leads to frustrating\u00a0user experience. Although there are a set of prior\u00a0approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and their OOD detection is context-independent which leads to suboptimal performance in dialog. The goal of\u00a0this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns\u00a0in the context of a dialog. For the sake of fostering further research, we also release new dialog datasets which are 3 publicly\u00a0available dialog corpora augmented with OOD turns in\u00a0a controllable way. Our method outperforms state-of-the-art dialog models equipped with a conventional OOD detection mechanism by a large margin in the presence of OOD utterances.<\/p>\n","protected":false},"excerpt":{"rendered":"
Neural dialog models often lack robustness to anomalous user\u00a0input and produce inappropriate responses which leads to frustrating\u00a0user experience. Although there are a set of prior\u00a0approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and their OOD detection is context-independent which leads to suboptimal performance in 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