Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning

Understand

Knowledge graph-based dialogue systems can narrow down knowledge candidates for generating informative and diverse responses with the use of prior information, e.g., triple attributes or graph paths.

• However, most current knowledge graph (KG) cover incomplete domain-specific knowledge.
• To overcome this drawback, we propose a knowledge graph based proactive dialogue generation model (KgDg) with three components, improved model-agnostic meta-learning algorithm (MAML), knowledge selection in knowledge triplets embedding, and knowledge aware proactive response generator.
• For knowledge triplets embedding and selection, we formulate it as a problem of sentence embedding to better capture semantic information.