Year: 2019
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/p19-1542
Institution: Hong Kong University of Science and Technology
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2945978556