The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning

04/15/2021

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by   Yuchen Lian, et al.

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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.