  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.
