Generating Personalized Dialogue via Multi-Task Meta-Learning

Understand

Conventional approaches to personalized dialogue generation typically require a large corpus, as well as predefined persona information.

• However, in a real-world setting, neither a large corpus of training data nor persona information are readily available.
• To address these practical limitations, we propose a novel multi-task meta-learning approach which involves training a model to adapt to new personas without relying on a large corpus, or on any predefined persona information.
• Instead, the model is tasked with generating personalized responses based on only the dialogue context.