Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review

TL;DR

This paper reviews recent advances in deep learning methods that incorporate knowledge for metaphor detection, highlighting key techniques, datasets, and future challenges in the field.

Contribution

It provides a comprehensive overview of knowledge injection techniques in deep learning models for metaphor detection, summarizing current methods, datasets, and future research directions.

Findings

Knowledge-assisted models outperform non-knowledge models in metaphor detection.

Recent studies have achieved state-of-the-art performance using knowledge injection.

The review identifies current challenges and potential future research directions.

Abstract

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.