Papers by Xianying Huang
C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| Challenge: | Recent studies have shown that graph convolutional networks (GCNs) can model syntactic information but incorrect syntaktic structure may introduce additional noise. |
| Approach: | They propose a graph convolutional network which integrates Contrastive Learning and Cooperative Learning with Prompt into GCN to alleviate the noise when modeling syntactic information. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and significantly outperformed existing models. |
Multi-Condition Guided Diffusion Network for Multimodal Emotion Recognition in Conversation (2025.findings-naacl)
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| Challenge: | Current research emphasizes contextual factors, the speaker’s influence, and extracting complementary information across different modalities. |
| Approach: | They propose a diffusion-based approach to address the challenges posed by redundant information and redundant information at the semantic level while robustly capturing shared semantics. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on two multimodal datasets and is generalizable and effective. |