Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution.

翻译：计算语言学的最新进展包括通过交互的神经网络智能体，从随机符号集出发模拟类人语言的涌现。近期提出的NeLLCom框架（Lian等人，2023）使智能体能够先学习人工语言，再利用其进行通信，旨在研究特定语言特性的涌现机制。我们通过引入更贴近现实的角色轮换智能体与群体通信机制，扩展了该框架（NeLLCom-X），以探究语言可学习性、交际压力与群体规模效应之间的相互作用。我们通过复现先前研究中关于词序/格标记权衡涌现的关键发现，验证了NeLLCom-X的有效性。进而，我们探究了交互作用如何影响语言趋同及权衡特征的涌现。这一新型框架为未来模拟多样化的语言特性提供了便利，凸显了交互与群体动力学在语言演化中的关键作用。