Infinite Dimensional Word Embeddings

We describe a method for learning word embeddings with stochastic dimensionality. Our Infinite Skip-Gram (iSG) model specifies an energy-based joint distribution over a word vector, a context vector, and their dimensionality, which can be defined over a countably infinite domain by employing the same techniques used to make the Infinite Restricted Boltzmann Machine (Cote & Larochelle, 2015) tractable. We find that the distribution over embedding dimensionality for a given word is highly interpretable and leads to an elegant probabilistic mechanism for word sense induction. We show qualitatively and quantitatively that the iSG produces parameter-efficient representations that are robust to language's inherent ambiguity.