This paper outlines a conceptual framework for understanding recent
developments in information retrieval and natural language processing that
attempts to integrate dense and sparse retrieval methods. I propose a
representational approach that breaks the core text retrieval problem into a
logical scoring model and a physical retrieval model. The scoring model is
defined in terms of encoders, which map queries and documents into a
representational space, and a comparison function that computes query-document
scores. The physical retrieval model defines how a system produces the top-$k$
scoring documents from an arbitrarily large corpus with respect to a query. The
scoring model can be further analyzed along two dimensions: dense vs. sparse
representations and supervised (learned) vs. unsupervised approaches. I show
that many recently proposed retrieval methods, including multi-stage ranking
designs, can be seen as different parameterizations in this framework, and that
a unified view suggests a number of open research questions, providing a
roadmap for future work. As a bonus, this conceptual framework establishes
connections to sentence similarity tasks in natural language processing and
information access "technologies" prior to the dawn of computing.

本文提出了一个概念框架来理解信息检索和自然语言处理中的最新发展，该框架试图将稠密和稀疏检索方法整合起来，并将文本检索问题分成逻辑评分模型和物理检索模型。作者提出度量器和比较函数，将查询和文档映射到表征空间，并计算查询 - 文档分数，同时分析了密集与稀疏表征和监督与无监督方法的影响。最后，作者提供了一个研究路线图，使得该框架更加清晰且提供未来工作的方向。