KNOWLEDGE-CONSISTENT DIALOGUE GENERATION WITH LANGUAGE MODELS AND KNOWLEDGE GRAPHS Anonymous authors Paper under double-blind review

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.

1. INTRODUCTION

Dialogue systems aim at conversing with humans by generating human-like responses, given the dialogue context. While pre-trained language models (PLMs) (Radford et al., 2019; Raffel et al., 2020) are capable of generating fluent responses, they often generate factually incorrect responses due to a lack of explicit knowledge (Shuster et al., 2021) . To overcome such limitations, recent methods access the external knowledge sources, such as Wikipedia (Dinan et al., 2019) or Web (Komeili et al., 2021) , and then retrieve the relevant knowledge for ongoing conversations. In addition to such document-based retrieval approaches, there also exists a variety of works (Tuan et al., 2019; Wu et al., 2020; Zhang et al., 2020a; Cui et al., 2021; Zhou et al., 2021; Galetzka et al., 2021; Li et al., 2022) , which focus on the use of the Knowledge Graphs (KGs) (Bollacker et al., 2008; Vrandecic & Krötzsch, 2014 ) -a different form of the knowledge source which succinctly encodes the knowledge in the most compact and effective form -in dialogue generation. Specifically, KGs consist of symbolic facts which represent entities as nodes and their relations as edges, in the triplet, e.g., (Pride & Prejudice, written by, Jane Austen) (See Figure 1 ), which can help generate a knowledge-grounded response. Most of the dialogue generation models with KGs (Galetzka et al., 2021; Li et al., 2022) utilize all the triplets associated with the entity in the dialogue context. However, not all of the facts are relevant to the ongoing conversation (e.g., Jane Austen was born in Steventon in Figure 1 ), which could mislead the models from generating factually incorrect responses. We found that about 87% of facts from 1-hop KG are irrelevant to the context in the OpendialKG dataset (Moon et al., 2019) . Moreover, encoding all the facts including the unnecessary ones is computationally inefficient (Galetzka et al., 2021; Rony et al., 2022) . On the other hand, even after correctly retrieving the relevant facts, it is not straightforward to combine two heterogeneous modalities: the dialogue context is represented as a text, meanwhile, the knowledge is represented as a graph. In other words, since PLMs already have tons of pre-trained parameters trained on the unstructured texts, properly conditioning the structured graph to PLMs is highly important. Otherwise, PLMs may generate inconsistent responses disregarding the knowledge from the retrieved subgraph, which is a phenomenon known as hallucination (Rohrbach et al., 2018) , where they generate responses with their own memorized yet incorrect knowledge.

