  Natural languages are powerful tools wielded by human beings to communicate
information and co-operate towards common goals. Their values lie in some main
properties like compositionality, hierarchy and recurrent syntax, which
computational linguists have been researching the emergence of in artificial
languages induced by language games. Only relatively recently, the AI community
has started to investigate language emergence and grounding working towards
better human-machine interfaces. For instance, interactive/conversational AI
assistants that are able to relate their vision to the ongoing conversation.
  This paper provides two contributions to this research field. Firstly, a
nomenclature is proposed to understand the main initiatives in studying
language emergence and grounding, accounting for the variations in assumptions
and constraints. Secondly, a PyTorch based deep learning framework is
introduced, entitled ReferentialGym, which is dedicated to furthering the
exploration of language emergence and grounding. By providing baseline
implementations of major algorithms and metrics, in addition to many different
features and approaches, ReferentialGym attempts to ease the entry barrier to
the field and provide the community with common implementations.
