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Artificial learners often behave differently from human learners in thecontext of neural agent-based simulations of language emergence and change. Acommon explanation is the lack of appropriate cognitive biases in theselearners. However, it has also been proposed that more naturalistic settings oflanguage learning and use could lead to more human-like results. We investigatethis latter account focusing on the word-order/case-marking trade-off, a widelyattested language universal that has proven particularly hard to simulate. Wepropose a new Neural-agent Language Learning and Communication framework(NeLLCom) where pairs of speaking and listening agents first learn a miniaturelanguage via supervised learning, and then optimize it for communication viareinforcement learning. Following closely the setup of earlier humanexperiments, we succeed in replicating the trade-off with the new frameworkwithout hard-coding specific biases in the agents. We see this as an essentialstep towards the investigation of language universals with neural learners.

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