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.

翻译：人工学习者在基于神经代理的语言涌现与演化模拟中常常表现出与人类学习者不同的行为。现有解释中，这些学习者缺乏适当的认知偏差是主流观点之一。然而，有研究提出，更贴近自然环境的语言学习与使用场景可能产生更接近人类的结果。本研究聚焦于语序与格标记的权衡现象——这一广泛验证的语言普遍性却极难通过模拟复现——对后一种解释进行探究。我们提出了新的神经代理语言学习与沟通框架（NeLLCom），在该框架中，成对的说话者与听者代理首先通过监督学习掌握给定微型语言，随后通过强化学习优化该语言以实现有效沟通。通过严格遵循早期人类实验的设置，我们在未对代理预置任何学习偏差的情况下，成功复现了该权衡现象。我们认为，这是利用神经学习者探究语言普遍性研究中的关键进展。