A Survey of Ontology Expansion for Conversational Understanding
원문 초록
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges. This survey aspires to be a foundational reference for researchers and practitioners, promoting further exploration and innovation in this crucial domain.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Survey on Prompt Tuning
A Survey on Latent Reasoning
A Survey on Vision-Language-Action Models for Autonomous Driving
From Tiny Machine Learning to Tiny Deep Learning: A Survey
A Comprehensive Survey on Continual Learning in Generative Models
A Survey on World Models Grounded in Acoustic Physical Information
토론
이 논문과 연결된 포럼 글이 아직 없습니다.