Title: Sharp Attention for Sequence to Sequence Learning. Abstract: Attention mechanism has been widely applied to tasks that output some sequence from an input image. Its success comes from the ability to align relevant parts of the encoded image with the target output. However, most of the existing methods fail to build clear alignment because the aligned parts are unable to well represent the target. In this paper we seek clear alignment in attention mechanism through a \emph{sharpener} module. Since it deliberately locates the target in an image region and refines representation to be target-specific, the alignment and interpretability of attention can be significantly improved. Experiments on synthetic handwritten digit as well as real-world scene text recognition datasets show that our approach outperforms the mainstream ones such as soft and hard attention.