Summary of Effective Context Modeling Framework For Emotion Recognition in Conversations, by Cuong Tran Van et al.
Effective Context Modeling Framework for Emotion Recognition in Conversations
by Cuong Tran Van, Thanh V. T. Tran, Van Nguyen, Truong Son Hy
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ConxGNN framework leverages Graph Neural Networks (GNNs) for Emotion Recognition in Conversations (ERC), capturing contextual information in conversations through two parallel modules: a multi-scale heterogeneous graph and a hypergraph. These modules are integrated into a fusion layer, where cross-modal attention is applied to produce a contextually enriched representation. Additionally, the framework tackles minority or semantically similar emotion classes by incorporating a re-weighting scheme into loss functions. Experimental results on IEMOCAP and MELD datasets demonstrate state-of-the-art performance compared to previous baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conversations can be tricky to understand because emotions are hidden behind words. A new way to recognize emotions in conversations uses special graphs called Graph Neural Networks (GNNs). These GNNs help computers learn from relationships between different parts of a conversation, like the speaker’s tone and what they’re saying. The new method, ConxGNN, has two main parts: one that looks at how each part of the conversation affects emotions, and another that connects all these parts together. This helps computers understand emotional changes in conversations more accurately. |
Keywords
» Artificial intelligence » Attention
