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.

提出了一个新的神经智能代理语言学习和通讯框架 (NeLLCom)，通过监督学习和强化学习来优化在自然语言学习和使用情况下语言学习的结果，成功实现了无任何学习偏见的代理之间单词顺序 / 格标记权衡的复制，这被视为深度研究语言普遍规律的重要一步。