  Word embedding parameters often dominate overall model sizes in neural
methods for natural language processing. We reduce deployed model sizes of text
classifiers by learning a hard word clustering in an end-to-end manner. We use
the Gumbel-Softmax distribution to maximize over the latent clustering while
minimizing the task loss. We propose variations that selectively assign
additional parameters to words, which further improves accuracy while still
remaining parameter-efficient.
