Papers with FEC
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. |
A Triple-View Framework for Fine-Grained Emotion Classification with Clustering-Guided Contrastive Learning (2025.acl-long)
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| Challenge: | Existing studies have focused on dealing with only one of the two difficulties of coarse-grained emotion classification. |
| Approach: | They propose a triple-view framework that treats FEC as an instance-label joint embedding learning problem to tackle both difficulties concurrently by considering three complementary views. |
| Outcome: | The proposed framework achieves significant and consistent improvements on two widely-used benchmark datasets. |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
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| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress . |
| Approach: | They propose a method that enhances few-shot FEC with a pivot task approach using large language models. |
| Outcome: | The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods . |
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework (2023.acl-long)
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| Challenge: | Current evaluations of FEC models that depend on factuality metrics are not reliable and detailed enough. |
| Approach: | They propose a fine-grained evaluation framework that automatically evaluates FEC models on different error categories. |
| Outcome: | The proposed evaluation framework compares models on different error categories and finds the best training modes and significant differences in the performance of existing models. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |