Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning
This work addresses the challenge of applying dialogue models to dynamic knowledge graphs, which is an incremental improvement for knowledge-based dialogue systems.
The paper tackles the problem of dialogue generation with dynamic knowledge graphs, which are sparse and incomplete, by proposing KDAD, a method that formulates dynamic knowledge triples as adversarial attacks and uses improved adversarial meta-learning. The result shows that the model significantly outperforms baselines and adapts extremely fast to dynamic knowledge with minimal training samples.
Knowledge graph-based dialogue systems are capable of generating more informative responses and can implement sophisticated reasoning mechanisms. However, these models do not take into account the sparseness and incompleteness of knowledge graph (KG)and current dialogue models cannot be applied to dynamic KG. This paper proposes a dynamic Knowledge graph-based dialogue generation method with improved adversarial Meta-Learning (KDAD). KDAD formulates dynamic knowledge triples as a problem of adversarial attack and incorporates the objective of quickly adapting to dynamic knowledge-aware dialogue generation. We train a knowledge graph-based dialog model with improved ADML using minimal training samples. The model can initialize the parameters and adapt to previous unseen knowledge so that training can be quickly completed based on only a few knowledge triples. We show that our model significantly outperforms other baselines. We evaluate and demonstrate that our method adapts extremely fast and well to dynamic knowledge graph-based dialogue generation.