The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis

TL;DR

This paper investigates probabilistic language acquisition through theoretical proofs, a practical learnability framework, and experimental validation, suggesting language learning can be driven by cognition-general principles rather than innate biases.

Contribution

It introduces a novel theoretical result on learning generative language models, a practical framework for quantifying language learnability, and experimental evidence supporting probabilistic acquisition from general cognition.

Findings

Theoretical proof of learning a wide class of language models from samples

A practical framework for predicting language learnability

Experimental results support probabilistic language acquisition from general principles

Abstract

There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.