Summary of Learning Retrieval Augmentation For Personalized Dialogue Generation, by Qiushi Huang et al.
Learning Retrieval Augmentation for Personalized Dialogue Generation
by Qiushi Huang, Shuai Fu, Xubo Liu, Wenwu Wang, Tom Ko, Yu Zhang, Lilian Tang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to personalized dialogue generation, called LAPDOG, is proposed in this paper. By leveraging persona profiles and dialogue context, the model generates highly tailored responses. However, existing persona profiles often lack comprehensive descriptions of the agent, making it challenging to generate truly personalized dialogues. To address this issue, the authors develop a learning retrieval augmentation method that incorporates external knowledge for persona dialogue generation. The LAPDOG model consists of a story retriever and a dialogue generator, which uses both the dialogue history and the augmented persona profile to generate responses. A joint training framework is used to optimize the model, and experiments on the CONVAI2 dataset show that LAPDOG outperforms baselines. The code for LAPDOG is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach to personalized dialogue generation called LAPDOG. It helps computers have more natural conversations by using information from a person’s profile and what has been talked about so far. Right now, these profiles are often very short, which makes it hard to generate truly personal conversations. To fix this problem, the authors created a new way of combining information from the profile and what has been said before. They tested their method on a big dataset and showed that it works better than other approaches. You can try using LAPDOG yourself by looking at its code online. |
