Few-shot text-based emotion detection
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Abstract (arXiv)
This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. With our final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
- 2023Large Language Model Guided Tree-of-Thoughtcited by 221
- 2025BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languagescited by 27
- 2024Transformer based neural networks for emotion recognition in conversationscited by 2
- 2024Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detectioncited by 2
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