Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
This addresses the challenge of producing more accurate and relevant responses in both goal and non-goal oriented dialogue systems, representing an incremental improvement over existing methods.
The paper tackled the problem of generating knowledge-grounded responses in dialogue systems by integrating Knowledge Graphs (KGs) into the response generation process, achieving better knowledge groundedness with an Entity F1 score compared to state-of-the-art models.
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge. Knowledge Graphs (KG) can be viewed as an abstraction of the real world, which can potentially facilitate a dialogue system to produce knowledge grounded responses. However, integrating KGs into the dialogue generation process in an end-to-end manner is a non-trivial task. This paper proposes a novel architecture for integrating KGs into the response generation process by training a BERT model that learns to answer using the elements of the KG (entities and relations) in a multi-task, end-to-end setting. The k-hop subgraph of the KG is incorporated into the model during training and inference using Graph Laplacian. Empirical evaluation suggests that the model achieves better knowledge groundedness (measured via Entity F1 score) compared to other state-of-the-art models for both goal and non-goal oriented dialogues.