An anatomy for neural search engines

Abstract

In this work, we explore the application of modern deep learning techniques to build a neural model centric search engine. We conduct an in-depth discussion under several quantitative and qualitative criteria, comparing the trade-offs of adopting the proposed neural architecture against the successful and mature traditional information retrieval techniques. We show that a full neural architecture, which employs neural models both in the retrieval and ranking phases, offers good scalability, predictability and evolution properties, and discuss under which conditions one can achieve state-of-the-art results. We conclude that deep learning centric systems still require significant more effort to implement and deploy and demand more computational resources, but this work, together with several others in the research community, sheds a light into that path.