Knowledge-aware path: interpretable graph reasoning in proactive dialogue generation

Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph(KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path(KAP) model was proposed for knowledge reasoning in proactive dialogue generation.KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.