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Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning
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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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Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding
A new multi-label emotion benchmark for four Ethiopian languages shows that fine-tuned encoder-only models outperform zero-shot and few-shot large language models, with large gaps between resource-rich and resource-po...
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