A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems - Concepedia

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A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems

DOI Full Paper Access

17

Citations

40

References

2021

Year

Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sung‐Jin Lee, Young‐Bum Kim, Spyros Matsoukas, Ruhi Sarikaya

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Artificial IntelligenceEngineeringPart-of-speech TaggingSpoken Dialog SystemIntelligent SystemsLanguage ProcessingText MiningNatural Language ProcessingApplied LinguisticsSyntaxComputational LinguisticsConversation AnalysisEmpirical MethodsLanguage StudiesLarge Ai ModelDialogue ManagementNatural Language InterfaceNlp TaskComputer ScienceNatural Language UnderstandingScalable FrameworkHan LiLinguisticsPo TaggingConversational Artificial Intelligence

Abstract

Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

References

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