LEARNING TOKEN IMPORTANCE USING MULTI-MODEL STOCHASTIC SPARSITY INDUCING REGULARIZATION

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving the representation of items of a vocabulary in an embedding space for use in machine learning models. An embedding matrix is generated wherein each row in the embedding matrix is a vector of elements and corresponds to an item of a vocabulary. A score is assigned to each vector in the embedding matrix indicating a probability of its corresponding vector being used in the machine learning model. The scores are iteratively updated by sampling a proper subset of vectors and updating the elements of each respective vector in the proper subset of vectors based on the respective scores of vectors. The score of each vector are then updated based on a loss function of the machine learning model. The embedding matrix is then re-structured based on the updated scores of the vectors.