Summary of Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning, by Kaipeng Wang et al.
Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning
by Kaipeng Wang, Zhi Jing, Yongye Su, Yikun Han
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method enhances classification performance on the GoEmotions dataset, a large-scale manually annotated benchmark for emotion detection in text. The paper tackles the challenge of detecting subtle emotions in text, a crucial problem in Natural Language Processing (NLP) with significant practical applications. By leveraging various techniques and analyzing performances across datasets, this study provides valuable insights into addressing the challenges of emotion detection in text, suggesting directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves emotion detection in text by tackling the challenge of detecting subtle emotions on the GoEmotions dataset. The goal is to enhance classification performance and provide insights for future research. This is important because emotion detection can be used to improve chatbots, customer service, and more. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Nlp
