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While natural languages are compositional, how state-of-the-art neural modelsachieve compositionality is still unclear. We propose a deep network, which notonly achieves competitive accuracy for text classification, but also exhibitscompositional behavior. That is, while creating hierarchical representations ofa piece of text, such as a sentence, the lower layers of the network distributetheir layer-specific attention weights to individual words. In contrast, thehigher layers compose meaningful phrases and clauses, whose lengths increase asthe networks get deeper until fully composing the sentence.

A Deep Network with Visual Text Composition Behavior