Why Verbs are Harder to Learn than Nouns: Initial Insights from a Computational Model of Intention Recognition in Situated Word Learning

We present a computational model that uses intention recognition as a basis for situated word learning. In an initial experiment, the model acquired a lexicon from situated natural language collected from human participants interacting in a virtual game environment. Similar to child language learning, the model learns nouns faster than verbs. In the model, this is due to inherent ambiguities in mapping verbs to inferred intentional structures. Since children must overcome similar ambiguities, the model provides a possible explanation for learning patterns in children.