Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.12291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909176077221888 |
|---|---|
| author | Lin, Yucheng Xia, Yuhan Long, Yunfei |
| author_facet | Lin, Yucheng Xia, Yuhan Long, Yunfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12291 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Augmenting emotion features in irony detection with Large language modeling Lin, Yucheng Xia, Yuhan Long, Yunfei Computation and Language Artificial Intelligence 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. |
| title | Augmenting emotion features in irony detection with Large language modeling |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.12291 |