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.
@article{arxiv.1905.10033,
title = {Personalizing Dialogue Agents via Meta-Learning},
author = {Zhaojiang Lin and Andrea Madotto and Chien-Sheng Wu and Pascale Fung},
journal= {arXiv preprint arXiv:1905.10033},
year = {2019}
}
Comments
Accepted in ACL 2019. Zhaojiang Lin* and Andrea Madotto* contributed equally to this work