Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning
Kaipeng Wang, Zhi Jing, Yongye Su, Yikun Han
TLDR
This paper improves fine-grained emotion detection on the GoEmotions dataset using Large Language Models, data augmentation, and transfer learning.
Key contributions
- Improves fine-grained emotion detection on the GoEmotions dataset.
- Leverages Large Language Models for enhanced classification performance.
- Investigates data augmentation and transfer learning methods.
- Addresses challenges in detecting subtle emotions within text.
Why it matters
Emotion detection is crucial for NLP applications, especially for subtle emotions. This work provides valuable insights into improving performance on challenging datasets like GoEmotions, guiding future research.
Original Abstract
This paper delves into enhancing the classification performance on the GoEmotions dataset, a large, manually annotated dataset for emotion detection in text. The primary goal of this paper is to address the challenges of detecting subtle emotions in text, a complex issue in Natural Language Processing (NLP) with significant practical applications. The findings offer valuable insights into addressing the challenges of emotion detection in text and suggest directions for future research, including the potential for a survey paper that synthesizes methods and performances across various datasets in this domain.
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