Jinxiaojia Jinxiaojia
2024
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space
Yihong Tang
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Bo Wang
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Dongming Zhao
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Jinxiaojia Jinxiaojia
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Zhangjijun Zhangjijun
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Ruifang He
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Yuexian Hou
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Personalized Dialogue Generation (PDG) aims to create coherent responses according to roles or personas. Traditional PDG relies on external role data, which can be scarce and raise privacy concerns. Approaches address these issues by extracting role information from dialogue history, which often fail to generically model roles in continuous space. To overcome these limitations, we introduce a novel framework Models Roles from Personalized Dialogue History by Exploring and Utilizing Latent Space (MORPHEUS) through a three-stage training process. Specifically, we create a persona codebook to represent roles in latent space compactly, and this codebook is used to construct a posterior distribution of role information. This method enables the model to generalize across roles, allowing the generation of personalized dialogues even for unseen roles. Experiments on both Chinese and English datasets demonstrate that MORPHEUS enhances the extraction of role information, and improves response generation without external role data. Additionally, MORPHEUS can be considered an efficient fine-tuning for large language models.
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Co-authors
- Yihong Tang 1
- Bo Wang 1
- Dongming Zhao 1
- Zhangjijun Zhangjijun 1
- Ruifang He 1
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