Personalizing Dialogue Agents via Meta-Learning
Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, Pascale Fung · 2019
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.