Keywords: knowledge-grounded dialogue generation, knowledge graph
TL;DR: Knowledge-Consistent Dialogue Generation with Context-Relevant Subgraph Retrieval, Invariant Graph Encoding, and Graph-Text Contrastive Learning
Abstract: Pre-trained language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to the absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging the structured knowledge from Knowledge Graphs (KGs). However, existing methods do not guarantee that the model utilizes a relevant piece of knowledge from the KG before generating knowledge-consistent dialogues. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-consistent dialogues with a KG. Specifically, our method first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned on the retrieved subgraph. Then, it learns a latent representation space using contrastive learning which ensures that the generated texts have high similarity to the retrieved subgraphs. We validate the performance of our SURGE framework on the OpendialKG and KOMODIS datasets and show that our method generates high-quality dialogues that faithfully reflect the knowledge from the KG.
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