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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"IEEE","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"International Conference on Acoustics, Speech, and Signal Processing 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