Recurrent Neural Network (RNN) has been successfully applied in many sequence learning problems. Such as handwriting recognition, image description, natural language processing and video motion analysis. After years of development, researchers have improved the internal structure of the RNN and introduced many variants. Among others, Gated Recurrent Unit (GRU) is one of the most widely used RNN model. However, GRU lacks the capability of adaptively paying attention to certain regions or locations, so that it may cause information redundancy or loss during leaning. In this paper, we propose a RNN model, called Recurrent Attention Unit (RAU), which seamlessly integrates the attention mechanism into the interior of GRU by adding an attention gate. The attention gate can enhance GRU's ability to remember long-term memory and help memory cells quickly discard unimportant content. RAU is capable of extracting information from the sequential data by adaptively selecting a sequence of regions or locations and pay more attention to the selected regions during learning. Extensive experiments on image classification, sentiment classification and language modeling show that RAU consistently outperforms GRU and other baseline methods.

本文提出一种名为Recurrent Attention Unit的循环神经网络模型，它将注意机制融入了GRU的内部结构中并通过增加attention gate提高了GRU对于长期记忆的能力，对于序列数据能够通过自适应选择序列的区域或位置并在学习过程中更加关注选定的区域，实验结果表明RAU在图像分类、情感分类和语言建模等方面均优于GRU和其他基线方法。

循环注意力单元