Summary of Ecrc: Emotion-causality Recognition in Korean Conversation For Gcn, by J. K. Lee et al.
ECRC: Emotion-Causality Recognition in Korean Conversation for GCN
by J. K. Lee, T. M. Chung
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A deep learning-based approach is proposed to analyze emotions and their underlying causes in conversational contexts, leveraging both word- and sentence-level embeddings to overcome limitations of previous methods. A novel graph structure, emotion-causality recognition in conversation (ECRC) model, integrates bidirectional long short-term memory (Bi-LSTM) and graph neural network (GCN) models for Korean conversation analysis. The proposed model outperforms single-embedding-based models in multi-task learning tasks, achieving 74.62% accuracy for emotion classification and 75.30% for causality recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how people feel and why they say what they say is being developed. This method uses special kinds of computer programs called deep neural networks to analyze conversations. It’s like having a super-smart friend who can read between the lines and figure out what someone means. The researchers used two different approaches to try to make sense of conversations, but then they combined them in a new way that works better than either one alone. They tested it on Korean and English conversations and found that it was very good at understanding emotions and the reasons behind them. |
Keywords
» Artificial intelligence » Classification » Deep learning » Embedding » Gcn » Graph neural network » Lstm » Multi task
