Papers with fine-grained emotion classification
Semantic alignment in hyperbolic space for fine-grained emotion classification (2025.acl-srw)
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| Challenge: | Existing approaches to fine-grained emotion classification operate in Euclidean space, where the flat geometry makes it difficult to distinguish semantically similar label labels. |
| Approach: | They propose a semantic alignment framework that leverages the Lorentz model of hyperbolic space to embed text and label representations into hyperbolical space via the exponential map. |
| Outcome: | The proposed framework improves on two benchmark FEC datasets. |
Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification (2024.emnlp-main)
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| Challenge: | Emotion classification is an important task with applications in education, virtual reality, and robotics. |
| Approach: | They propose to use token embeddings to generate a "semantic-anchor graph" using semantic anchors, sentences can be projected onto them to form a graph . |
| Outcome: | Empirically, the proposed system can generate meaningful semantic anchors and discriminative graph patterns for different emotion. |
Linear Layer Extrapolation for Fine-Grained Emotion Classification (2024.emnlp-main)
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| Challenge: | Existing studies show that Transformer-based language models are more factual accurate in later layers . |
| Approach: | They propose a method that optimizes contrast based on the selected intermediate layer . they observe a similar pattern for fine-grained emotion classification in text . |
| Outcome: | Experiments show that the proposed method outperforms standard methods in fine-grained emotion classification tasks. |