Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off

Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. The lack of appropriate cognitive biases in these learners is one of the prevailing explanations. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. In this work, we investigate the latter account focusing on the word-order/case-marking trade-off, a widely attested language universal which has proven particularly difficult to simulate. We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a given miniature language through supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding any learning bias in the agents. We see this as an essential step towards the investigation of language universals with neural learners.

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

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.