Speaker: Steve Piantadosi
Affiliation: TedLab
Time: 3/18/08 at 12PM
Room: 46-3310
Lunch: Beauty’s Pizza
Title: A Bayesian model of compositional semantics acquisition
We present an unsupervised, cross-situational Bayesian learning model for the acquisition of compositional semantics. We show that the model acquires the correct grammar for a toy version of English using a psychologically-plausible amount of data, over a wide range of possible learning environments. By assuming that speakers typically produce sentences which are true in the world, the model learns the semantic representation of content and function words, using only positive evidence in the form of sentences and world contexts. We argue that the model can adequately solve both the problem of referential uncertainty and the subset problem in this domain, and show that the model makes mistakes analogous to those made by children.