Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated, and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.
翻译:关注模式现已成为神经网络中的一个重要概念,已在不同应用领域进行了研究,该调查对模拟关注方面的发展动态进行了有条理和全面的概述,特别是,我们提出了将现有技术分类为连贯分类的分类法,我们审查了已纳入关注的突出神经结构,并讨论了模拟关注已产生重大影响的应用方法,我们还介绍了如何利用关注提高神经网络的可解释性,最后,我们讨论了未来的研究方向,我们希望这一调查将简要介绍关注模式和指导从业人员,同时制定应用方法。