  As a sub-discipline of evolutionary and computational linguistics, emergent
communication (EC) studies communication protocols, called emergent languages,
arising in simulations where agents communicate. A key goal of EC is to give
rise to languages that share statistical properties with natural languages. In
this paper, we reinterpret Lewis's signaling game, a frequently used setting in
EC, as beta-VAE and reformulate its objective function as ELBO. Consequently,
we clarify the existence of prior distributions of emergent languages and show
that the choice of the priors can influence their statistical properties.
Specifically, we address the properties of word lengths and segmentation, known
as Zipf's law of abbreviation (ZLA) and Harris's articulation scheme (HAS),
respectively. It has been reported that the emergent languages do not follow
them when using the conventional objective. We experimentally demonstrate that
by selecting an appropriate prior distribution, more natural segments emerge,
while suggesting that the conventional one prevents the languages from
following ZLA and HAS.
