Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
This addresses the role of communication in language evolution for linguists and AI researchers, but it is incremental as it replicates and extends existing findings with a computational model.
The study tackled the problem of how Differential Case Marking (DCM) emerges in language by simulating it with neural-network agents, finding that learning alone does not lead to DCM, but communication causes differential use of markers to arise, supporting prior human experiments.
Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith & Culbertson, 2020). Multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human result. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith and Culbertson (2020)'s findings that highlight the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.