State-of-the-art deep neural networks leverage task-specific architectures to develop hierarchical representations of their input, with each layer building a refined abstraction of the layer that came before it<cite> (Conneau et al., 2016)</cite> . In a departure from this philosophy, we propose a divide-and-conquer approach, where a team of readers each focus on different aspects of the text, and then combine their representations to make a joint decision.