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Sparsity often leads to efficient and interpretable representations for data. In this paper, we introduce an architecture to infer the appropriate sparsity pattern for the word embeddings while learning the sentence composition in a deep network. The proposed approach produces competitive results in sentiment and topic classification tasks with high degree of sparsity. It is computationally cheaper to compute sparse word representations than existing approaches. The imposed sparsity is directly controlled by the task considered and leads to more interpretability.

Sparsifying Word Representations for Deep Unordered Sentence Modeling