  In previous work, artificial agents were shown to achieve almost perfect
accuracy in referential games where they have to communicate to identify
images. Nevertheless, the resulting communication protocols rarely display
salient features of natural languages, such as compositionality. In this paper,
we propose some realistic sources of pressure on communication that avert this
outcome. More specifically, we formalise the principle of least effort through
an auxiliary objective. Moreover, we explore several game variants, inspired by
the principle of object constancy, in which we alter the frequency, position,
and luminosity of the objects in the images. We perform an extensive analysis
on their effect through compositionality metrics, diagnostic classifiers, and
zero-shot evaluation. Our findings reveal that the proposed sources of pressure
result in emerging languages with less redundancy, more focus on high-level
conceptual information, and better abilities of generalisation. Overall, our
contributions reduce the gap between emergent and natural languages.
