  Natural languages display a trade-off among different strategies to convey
syntactic structure, such as word order or inflection. This trade-off, however,
has not appeared in recent simulations of iterated language learning with
neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in
light of three factors that play an important role in comparable experiments
from the Language Evolution field: (i) speaker bias towards efficient
messaging, (ii) non systematic input languages, and (iii) learning bottleneck.
Our simulations show that neural agents mainly strive to maintain the utterance
type distribution observed during learning, instead of developing a more
efficient or systematic language.
