NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication

Conference on Computational Natural Language Learning, pp. 243–258

Abstract

Recent advances in computational linguistics include simulating the emergence of humanlike 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.

Authors 3

1. Yuchen Lian

   Xi'an Jiaotong University

   Affiliation as printed

   Faculty of Electronic and Information Engineering , Xi'an Jiaotong University

2. Tessa Verhoef Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science , Leiden University

3. Arianna Bisazza

   University of Groningen

   Affiliation as printed

   Center for Language and Cognition , University of Groningen