  Multi-stage ranking pipelines have been a practical solution in modern search
systems, where the first-stage retrieval is to return a subset of candidate
documents, and latter stages attempt to re-rank those candidates. Unlike
re-ranking stages going through quick technique shifts during past decades, the
first-stage retrieval has long been dominated by classical term-based models.
Unfortunately, these models suffer from the vocabulary mismatch problem, which
may block re-ranking stages from relevant documents at the very beginning.
Therefore, it has been a long-term desire to build semantic models for the
first-stage retrieval that can achieve high recall efficiently. Recently, we
have witnessed an explosive growth of research interests on the first-stage
semantic retrieval models. We believe it is the right time to survey current
status, learn from existing methods, and gain some insights for future
development. In this paper, we describe the current landscape of the
first-stage retrieval models under a unified framework to clarify the
connection between classical term-based retrieval methods, early semantic
retrieval methods and neural semantic retrieval methods. Moreover, we identify
some open challenges and envision some future directions, with the hope of
inspiring more researches on these important yet less investigated topics.
