综述对话理解中的本体扩展技术，助对话系统应对新需求。
A Survey of Ontology Expansion for Conversational Understanding
- 按新意图、新槽位值、联合扩展三类梳理前沿方法
- 指出当前挑战：评估标准不统一、真实场景适应性弱
- 适合对话系统研发者与本体构建研究者参考
在快速发展的对话人工智能领域，本体扩展（OnExp）对于提升对话代理的适应性和鲁棒性至关重要。传统模型依赖静态预定义本体，难以应对用户的新需求和未预见情况。本文全面回顾了对话理解中本体扩展的最新技术，将现有文献分为三类：(1) 新意图发现，(2) 新槽位-值发现，(3) 联合本体扩展。通过分析各类方法、基准测试及面临挑战，本文揭示了若干新兴研究方向，旨在提升代理在真实场景下的表现，并讨论其对应难题。本综述旨在成为研究人员与实践者的基础参考，推动该关键领域的进一步探索与创新。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。