Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI

TL;DR

This paper presents a low-cost framework for generating relevance datasets and metrics to evaluate entity resolution in voice assistants, enhancing interpretability and diagnostic capabilities.

Contribution

It introduces a novel relevance generation method using customer feedback signals and new metrics for comprehensive ER performance evaluation.

Findings

Generated relevance datasets effectively evaluate ER systems.

Metrics provide detailed insights into ER performance issues.

Framework reduces costs compared to traditional methods.

Abstract

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.