Papers › Personalizing Dialogue Agents via Meta-Learning

Personalizing Dialogue Agents via Meta-Learning

24 May 2019ACL 2019 7arXiv:1905.10033archive 2025-07-28

Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung

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-meta-learning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.

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