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• W2805024782 abstract "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." @default.
• W2805024782 created "2018-06-01" @default.
• W2805024782 creator A5070933659 @default.
• W2805024782 creator A5081022650 @default.
• W2805024782 date "2018-01-01" @default.
• W2805024782 modified "2023-09-26" @default.
• W2805024782 title "Smaller Text Classifiers with Discriminative Cluster Embeddings" @default.
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• W2805024782 doi "https://doi.org/10.18653/v1/n18-2116" @default.
• W2805024782 hasPublicationYear "2018" @default.
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