Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process
This work addresses the challenge of understanding and predicting lexicon dynamics in emergent language experiments, which is incremental as it builds on existing theoretical models.
The authors tackled the problem of modeling emergent lexicon formation by introducing FiLex, a self-reinforcing stochastic process, and empirically tested its ability to capture the relationship between hyperparameters and Shannon entropy in emergent language systems.
We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments. The central property of FiLex is that it is a self-reinforcing process, parallel to the intuition that the more a word is used in a language, the more its use will continue. As a theoretical model, FiLex serves as a way to both explain and predict the behavior of the emergent language system. We empirically test FiLex's ability to capture the relationship between the emergent language's hyperparameters and the lexicon's Shannon entropy.