Irony Detection, Reasoning and Understanding in Zero-shot Learning
This work addresses the problem of irony detection for natural language processing applications, but it is incremental as it builds on existing LLM methods with specific prompts.
The study tackled the challenge of generalizing irony detection across diverse real-world scenarios by using irony-focused prompts from the IDADP framework for LLMs, which improved performance and generated coherent reasoning to transform ironic text into its intended meaning.
The generalisation of irony detection faces significant challenges, leading to substantial performance deviations when detection models are applied to diverse real-world scenarios. In this study, we find that irony-focused prompts, as generated from our IDADP framework for LLMs, can not only overcome dataset-specific limitations but also generate coherent, human-readable reasoning, transforming ironic text into its intended meaning. Based on our findings and in-depth analysis, we identify several promising directions for future research aimed at enhancing LLMs' zero-shot capabilities in irony detection, reasoning, and comprehension. These include advancing contextual awareness in irony detection, exploring hybrid symbolic-neural methods, and integrating multimodal data, among others.