Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities
원문 초록
Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. We discuss how query domain classification, knowledge graphs and user interaction data, and query personalization can be helpful to improve the accurate recognition of spoken information domain queries. Finally, we also provide a brief overview of current problems and challenges in speech recognition.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
JointRank: Rank Large Set with Single Pass
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.