Augmenting emotion features in irony detection with Large language
  modeling
Yucheng Lin Yuhan Xia Yunfei Long
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.