Metaphor as an advanced cognitive modality works by extracting familiar concepts in the target domain in order to understand vague and abstract concepts in the source domain. This helps humans to quickly understand and master new domains and thus adapt to changing environments. With the continuous development of metaphor research in the natural language community, many studies using knowledge-assisted models to detect textual metaphors have emerged in recent years. Compared to not using knowledge, systems that introduce various kinds of knowledge achieve greater performance gains and reach SOTA in a recent study. Based on this, the goal of this paper is to provide a comprehensive review of research advances in the application of deep learning for knowledge injection in metaphor detection tasks. We will first systematically summarize and generalize the mainstream knowledge and knowledge injection principles. Then, the datasets, evaluation metrics, and benchmark models used in metaphor detection tasks are examined. Finally, we explore the current issues facing knowledge injection methods and provide an outlook on future research directions.

翻译：隐喻作为一种高级认知方式，通过提取目标域中的熟悉概念来理解源域中的模糊和抽象概念，从而帮助人类快速掌握新领域并适应环境变化。随着自然语言社区中隐喻研究的不断发展，近年来涌现出大量利用知识辅助模型检测文本隐喻的研究。与未引入知识的方法相比，引入多种知识的系统在性能上取得了更大提升，并在近期研究中达到当前最优水平。基于此，本文旨在全面综述深度学习在隐喻检测任务中知识注入的研究进展。首先系统总结并归纳主流知识及其注入原理，继而梳理隐喻检测任务中使用的数据集、评估指标及基准模型，最后探讨当前知识注入方法面临的挑战，并对未来研究方向进行展望。