Personalizing Dialogue Agents via Meta-Learning
Open accessAbstract
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.Collecting such descriptions from existing dialogues is expensive and requires hand-crafted feature designs.In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017) to personalized dialogue learning without using any persona descriptions.Our model learns to quickly adapt to new personas by leveraging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions.Empirical results on Persona-chat dataset (Zhang et al., 2018) indicate that our solution outperforms non-metalearning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.
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Meta-learning approaches enable rapid adaptation to new persona data.
“Meta-learning approaches [19] enable rapid adaptation to new persona data, raising questions in multi-user systems about whether adaptations should remain private or be shared.”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [19]
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- Authors
- Madotto, A., Lin, Z., Wu, C.-S., & Fung, P.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 5454-5459
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2945978556
- Cited by
- 183
- References
- 6
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 11 · 2023: 33 · 2022: 28 · 2021: 41 · 2020: 40 · 2019: 5
- FWCI
- 10.67
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Hong Kong University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- meta-learning, model-agnostic meta-learning, few-shot adaptation
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