Abstract
This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation. Traditional irony detection techniques typically fall short due to their reliance on static linguistic features and predefined knowledge bases, often overlooking the nuanced emotional dimensions integral to irony. In contrast, our methodology augments the detection process by integrating subtle emotional cues, augmented through LLMs, into three benchmark pre-trained NLP models - BERT, T5, and GPT-2 - which are widely recognized as foundational in irony detection. We assessed our method using the SemEval-2018 Task 3 dataset and observed substantial enhancements in irony detection capabilities.
Abstract (translated)
本研究介绍了一种新颖的 Irony 检测方法，该方法采用基于提示的学习方法（LLMs）来促进情感中心化文本增强。传统的 Irony 检测技术通常因为其依赖静态语言特征和预定义知识库而不足，往往忽视了 Irony 中至关重要的细微情感维度。相比之下，我们的方法通过将微妙的情感线索通过 LLMs 增强，将三种广泛认为是 Irony 检测基础的预训练 NLP 模型 - BERT、T5 和 GPT-2 - 集成到检测过程中，从而增强了 Irony 检测能力。我们对该方法使用 SemEval-2018 任务 3 数据集进行了评估，并观察到 Irony 检测能力得到了显著提升。
URL
https://arxiv.org/abs/2404.12291
