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Word embedding parameters often dominate overall model sizes in neuralmethods for natural language processing. We reduce deployed model sizes of textclassifiers by learning a hard word clustering in an end-to-end manner. We usethe Gumbel-Softmax distribution to maximize over the latent clustering whileminimizing the task loss. We propose variations that selectively assignadditional parameters to words, which further improves accuracy while stillremaining parameter-efficient.

Smaller Text Classifiers with Discriminative Cluster Embeddings