论文标题
AI中实体分辨率的低成本相关性生成和评估指标
Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI
论文作者
论文摘要
语音助手中的实体分辨率（ER）是运行时间内的主要组成部分，可以将用户中的实体索要到现实世界实体。 ER涉及两个主要功能1。相关性生成和2。排名。在本文中，我们通过使用客户隐式和明确的反馈信号生成功能来提出低成本相关性生成框架。生成的相关性数据集可以用作测试集合以衡量ER性能。我们还引入了一组指标，这些指标可以准确地测量各个维度的ER系统性能。它们为深度潜水和确定ER问题的根本原因提供了极大的解释性，无论问题是在相关性的产生还是排名中。
Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking. In this paper we propose a low cost relevance generation framework by generating features using customer implicit and explicit feedback signals. The generated relevance datasets can serve as test sets to measure ER performance. We also introduce a set of metrics that accurately measures the performance of ER systems in various dimensions. They provide great interpretability to deep dive and identifying root cause of ER issues, whether the problem is in relevance generation or ranking.
