Posterior Sampling of Probabilistic Word Embeddings

Väinö Yrjänäinen, Isac Boström, Måns Magnusson +1

Quantifying uncertainty in word embeddings is crucial for reliable inference from textual data. However, existing Bayesian methods such as Hamiltonian Monte Carlo (HMC) and mean-fi…