Summary of Personalized Topic Selection Model For Topic-grounded Dialogue, by Shixuan Fan et al.
Personalized Topic Selection Model for Topic-Grounded Dialogue
by Shixuan Fan, Wei Wei, Xiaofei Wen, Xianling Mao, Jixiong Chen, Dangyang Chen
First submitted to arxiv on: 4 Jun 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 paper proposes a novel Personalized Topic Selection model for Topic-grounded Dialogue (PETD) to build user-engaging and coherent dialogue agents. Current models neglect the interaction between side information sources like topics and personas, leading to predicting irrelevant topics. PETD addresses this issue by selectively aggregating side information based on their correlation with global topics and personas. The method uses a contrastive learning-based persona selector to filter out irrelevant personas without annotations. Experimental results show that PETD outperforms state-of-the-art baselines across various evaluation metrics, generating engaging and diverse responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating better chatbots by understanding what users want to talk about. Right now, chatbots can be pretty boring because they don’t really understand what’s going on in the conversation. The researchers are trying to fix this problem by making a new system that takes into account different types of information, like topics and personas. They’re doing this to make sure the chatbot is talking about something interesting and relevant to the user. The paper shows that their new method works better than other methods and creates more engaging conversations. |
