  Transformers are arguably the main workhorse in recent Natural Language
Processing research. By definition a Transformer is invariant with respect to
reorderings of the input. However, language is inherently sequential and word
order is essential to the semantics and syntax of an utterance. In this paper,
we provide an overview of common methods to incorporate position information
into Transformer models. The objectives of this survey are to i) showcase that
position information in Transformer is a vibrant and extensive research area;
ii) enable the reader to compare existing methods by providing a unified
notation and meaningful clustering; iii) indicate what characteristics of an
application should be taken into account when selecting a position encoding;
iv) provide stimuli for future research.
