  The vocabulary mismatch problem is a long-standing problem in information
retrieval. Semantic matching holds the promise of solving the problem. Recent
advances in language technology have given rise to unsupervised neural models
for learning representations of words as well as bigger textual units. Such
representations enable powerful semantic matching methods. This survey is meant
as an introduction to the use of neural models for semantic matching. To remain
focused we limit ourselves to web search. We detail the required background and
terminology, a taxonomy grouping the rapidly growing body of work in the area,
and then survey work on neural models for semantic matching in the context of
three tasks: query suggestion, ad retrieval, and document retrieval. We include
a section on resources and best practices that we believe will help readers who
are new to the area. We conclude with an assessment of the state-of-the-art and
suggestions for future work.
