2021/10/19 by Wei Xiao, Xiao, Wei, Qian Hu +12
Computer Science · #AI in Service Interactions #Ambiguity #Artificial intelligence #Baseline (sea) #Component (thermodynamics) #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Human–computer interaction #Information Retrieval (cs.IR) #Information retrieval #Machine Learning (cs.LG) #Natural language processing #Recommender Systems and Techniques #Simple (philosophy) #Speech and dialogue systems #Task (project management) #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.09877
9 pages, IRS KDD workshop 2021
arxiv created 2021/10/19 · openalex publication_date 2021/10/19 · arxiv updated 2021/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like "Sorry, I don't know" hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction.