​Efficiency Tradeoffs in the Design of Neural Search Systems

​Information retrieval (IR) - the challenge of connecting users to previously stored relevant information - has received renewed attention of late due to the advent of pretrained transformer-based models. In recent years, we have seen the introduction of many new types of models (e.g., dense and sparse learned representations, cross-encoders, etc.) in the context of techniques that have been around for decades (e.g., BM25, multi-stage ranking, etc.). What does it mean for a search system to be efficient? In this talk, I'll try to sort through efficiency tradeoffs in the design and construction of end-to-end search systems, organized along the dimensions of time, space, and cost.