SmallER: Scaling neural entity resolution for edge devices

In this paper we introduce SmallER, a scalable neural entity resolution system capable of running directly on edge devices. SmallER addresses constraints imposed by the on-device setting such as bounded memory consumption for both model and catalog storage, limited compute resources, and related latency challenges introduced by those restrictions. Our model includes
distinct modules to learn syntactic and semantic information and is trained to handle multiple domains within one compact architecture. We use compressed tries to reduce the space required to store catalogs and a novel implementation of spatial partitioning trees to strike a balance between reducing runtime latency and preserving recall relative to full catalog search. Our final model consumes only 3MB of memory at inference time with classification accuracy surpassing that of previously established, domain-specific baseline models on live customer utterances. For the largest catalogs we consider (300 or more entries), our proxy metric for runtime latency is reduced by more than 90%.