A Grounded Approach to Modeling Generic Knowledge Acquisition

TL;DR

This paper presents a cognitively plausible model that enhances grounded language acquisition by incorporating a concept network to learn and represent generic knowledge about categories.

Contribution

The paper introduces a novel concept network layer to an existing framework, enabling the modeling of generic knowledge acquisition in language learning systems.

Findings

The extended model successfully learns generic information from language data.

The concept network improves the system's ability to encode and relate concepts.

Demonstrated effectiveness across three language acquisition tasks.

Abstract

We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.