  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.
