Robustness of the Random Language Model

Author:

Fatemeh Lalegani, Eric De Giuli

Keyword:

Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn), Computation and Language (cs.CL)

journal:

date:

2023-09-25 16:00:00

Abstract

The Random Language Model (De Giuli 2019) is an ensemble of stochastic context-free grammars, quantifying the syntax of human and computer languages. The model suggests a simple picture of first language learning as a type of annealing in the vast space of potential languages. In its simplest formulation, it implies a single continuous transition to grammatical syntax, at which the symmetry among potential words and categories is spontaneously broken. Here this picture is scrutinized by considering its robustness against explicit symmetry breaking, an inevitable component of learning in the real world. It is shown that the scenario is robust to such symmetry breaking. Comparison with human data on the clustering coefficient of syntax networks suggests that the observed transition is equivalent to that normally experienced by children at age 24 months.

PDF: Robustness of the Random Language Model.pdf