Deep Learning has already proven to be the primary technique to address a number of problems. It holds further promise in solving more challenging problems if we can overcome obstacles, such as the lack of quality training data and poor interpretability. The exploitation of domain knowledge and application semantics can enhance existing deep learning methods by infusing relevant conceptual information into a statistical, data-driven computational approach. This will require resolving the impedance mismatch due to different representational forms and abstractions between symbolic and statistical AI techniques. In this article, we describe a continuum that comprises of three stages for infusion of knowledge into the machine/deep learning architectures. As this continuum progresses across these three stages, it starts with shallow infusion in the form of embeddings, and attention and knowledge-based constraints improve with a semideep infusion. Toward the end reflecting deeper incorporation of knowledge, we articulate the value of incorporating knowledge at different levels of abstractions in the latent layers of neural networks. While shallow infusion is well studied and semideep infusion is in progress, we consider Deep Infusion of Knowledge as a new paradigm that will significantly advance the capabilities and promises of deep learning.

中文翻译：

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用于增强深度学习的知识注入学习的阴影

深度学习已被证明是解决许多问题的主要技术。如果我们能够克服障碍，例如缺乏高质量的训练数据和可解释性差，它在解决更具挑战性的问题方面有更大的希望。领域知识和应用语义的开发可以通过将相关概念信息注入到统计的、数据驱动的计算方法中来增强现有的深度学习方法。这将需要解决由于符号和统计 AI 技术之间的不同表示形式和抽象而导致的阻抗失配。在本文中，我们描述了一个连续体，它包括将知识注入机器/深度学习架构的三个阶段。随着这个连续体跨越这三个阶段，它从嵌入形式的浅层注入开始，而注意力和基于知识的约束则通过半深度注入进行改进。最后，我们阐明了在神经网络的潜在层中在不同抽象层次上合并知识的价值，这反映了知识的更深层次的结合。虽然浅层注入得到了很好的研究，半深度注入正在进行中，但我们认为知识的深度注入是一种新范式，它将显着提高深度学习的能力和前景。