Reinforcement Learning Over Knowledge Graphs for Explainable Dialogue Intent Mining

• Main Authors: Kai Yang, Xinyu Kong, Yafang Wang, Jie Zhang, Gerard De Melo
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Knowledge graph; dialogue intent mining; reinforcement learning
• Online Access: https://ieeexplore.ieee.org/document/9083954/

In light of the millions of households that have adopted intelligent assistant powered devices, multi-turn dialogue has become an important field of inquiry. Most current methods identify the underlying intent in the dialogue using opaque classification techniques that fail to provide any interpretable basis for the classification. To address this, we propose a scheme to interpret the intent in multi-turn dialogue based on specific characteristics of the dialogue text. We rely on policy-guided reinforcement learning to identify paths in a graph to confirm concrete paths of inference that serve as interpretable explanations. The graph is induced based on the multi-turn dialogue user utterances, the intents, i.e., standard queries of the dialogues, and the sub-intents associated with the dialogues. Our reinforcement learning method then discerns the characteristics of the dialogue in chronological order as the basis for multi-turn dialogue path selection. Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best.