Summary of Ctsm: Combining Trait and State Emotions For Empathetic Response Model, by Wang Yufeng et al.
CTSM: Combining Trait and State Emotions for Empathetic Response Model
by Wang Yufeng, Chen Chao, Yang Zhou, Wang Shuhui, Liao Xiangwen
First submitted to arxiv on: 22 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 The proposed Combining Trait and State emotions for Empathetic Response Model (CTSM) aims to improve dialogue systems’ ability to perceive speakers’ emotions and generate empathetic responses. The model combines trait and state emotion embeddings, as well as an emotion guidance module, to enhance emotional perception. A cross-contrastive learning decoder is also proposed to align trait and state emotions between generated responses and contexts. Evaluation results show that CTSM outperforms state-of-the-art baselines in generating empathetic responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new model called CTSM that helps chatbots understand people’s feelings and respond in a way that shows empathy. This is important because previous models only looked at one type of emotion, but real-life conversations involve many different emotions. The model uses two types of emotional representations: trait emotions (which are constant) and state emotions (which change depending on the situation). It also has an “emotion guidance” feature that helps the model better understand emotions. To make sure the model is good at generating empathetic responses, it’s trained using a special technique called cross-contrastive learning. The results show that CTSM outperforms other models in terms of generating empathetic responses. |
Keywords
» Artificial intelligence » Decoder
