Augmenting emotion features in irony detection with Large language modeling

arXiv:2404.12291v1 Announce Type: new
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, …

abstract arxiv augmentation cs.ai cs.cl detection emotion features irony knowledge language language models large language large language models llms modeling novel prompt prompt-based learning reliance study text type