  Natural languages commonly display a trade-off among different strategies to
convey constituent roles. A similar trade-off, however, has not been observed
in recent simulations of iterated language learning with neural network based
agents (Chaabouni et al., 2019b). In this work, we re-evaluate this result in
the light of two important factors, namely: the lack of effort-based pressure
in the agents and the lack of variability in the initial input language.
