Augmenting emotion features in irony detection with Large language modeling
This addresses the problem of improving irony detection for NLP applications by focusing on emotional nuances, though it appears incremental as it builds on existing models.
The study tackled irony detection by integrating emotion features augmented through Large Language Models into pre-trained NLP models like BERT, T5, and GPT-2, resulting in substantial enhancements in detection capabilities on the SemEval-2018 Task 3 dataset.
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.