Summary of Stickerconv: Generating Multimodal Empathetic Responses From Scratch, by Yiqun Zhang et al.
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
by Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, Lingshuai Wang, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
First submitted to arxiv on: 20 Jan 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 Agent4SC model uses collaborative agent interactions to simulate human behavior with sticker usage, enhancing multimodal empathetic communication. This paper introduces a comprehensive dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. The authors also propose PEGS, a multimodal empathetic response generation framework, which outperforms baseline models in generating contextually relevant and emotionally resonant responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stickers are used to help people understand and feel empathy when talking online. But there’s not much research on this topic because it’s hard to make a big dataset for testing. This paper introduces a new model called Agent4SC that uses stickers in conversations, making them more realistic and empathetic. The authors also created a big dataset with many different sticker combinations, which can be used to test other models too. They even developed a special tool called PEGS that helps generate responses that are sensitive to the conversation’s emotional tone. |
