  Probing classifiers have emerged as one of the prominent methodologies for
interpreting and analyzing deep neural network models of natural language
processing. The basic idea is simple -- a classifier is trained to predict some
linguistic property from a model's representations -- and has been used to
examine a wide variety of models and properties. However, recent studies have
demonstrated various methodological limitations of this approach. This article
critically reviews the probing classifiers framework, highlighting their
promises, shortcomings, and advances.
