GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation
Derek Chen, Zhou Yu
2021Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing18 citationsDOIOpen Access PDF
Abstract
Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples.
Topics & Concepts
Scope (computer science)Computer scienceKey (lock)Focus (optics)Process (computing)DetectorData miningTraining setArtificial intelligenceMachine learningTelecommunicationsProgramming languageOperating systemComputer securityOpticsPhysicsSpeech and dialogue systemsTopic ModelingNatural Language Processing Techniques