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Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning

arXiv 2403.06108 ↗First posted 10 Mar 2024cs.CL cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 10 Mar 2024.

Builds on

15 of 39 references.

References · 15

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  11. 2024When Large Language Models Meet Vector Databases: A Surveycited by 39
  12. 2021ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningcited by 18
3 more
  1. 2020Variational Autoencoder for Anti-Cancer Drug Response Predictioncited by 5
  2. 2023CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Modelscited by 3
  3. 2017EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensitycited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generationcited by 0