EARL : Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021)

Abstract: Generating informative and appropriate responses is challenging but important for building human-like dialogue systems. Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge that infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation. In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation. Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.

## introduction generating informative and appropriate responses is vital for the success of human-like dialogue systems. to this end, there has been a rising tendency in enhancing conversation models with external knowledge recently, which is well-known as the knowledge-grounded conversation model @xcite @xcite . several studies incorporate unstructured texts, such as web pages @xcite and wikipedia articles @xcite , as the external knowledge to generate informative responses. some work introduces structured knowledge, e.g. the knowledge graph @xcite prior studies adopt either pre-trained knowledge graph embeddings @xcite , e.g. transe @xcite , word embeddings @xcite , or adjacency matrix @xcite to model entities and relations in knowledge graphs and incorporate them to conversation generation. these models face two major challenges when applied to introduce large-scale knowledge graphs. first, there is a significant gap in representations between knowledge and text @xcite , which requires model training to apply knowledge in conversation generation based on different knowledge representations. however, the training corpus of knowledgegrounded conversations only contains a small subset of entities for applying knowledge, while the large-scale untrained entities are difficult to be utilized due to the gap between their representations. second, it is extremely challenging to represent millions of entities and triples of large-scale knowledge graphs (see to address these issues, we propose earl, an entity-agnostic representation learning method to incorporate knowledge graphs into informative conversation generation, which can be easily integrated into existing conversation frameworks, such as seq2seq @xcite , hred @xcite , and transformer @xcite . the intuition lies in that knowledge graphs have sparse entities but dense relations, e.g. con-ceptnet @xcite contains over 8 million entities while only 36 relations, as shown in specifically, earl addresses the issues mentioned above in three ways: (1) a delexicalization step replaces the entities in the conversation history with mask tokens, which makes it entity-agnostic to the conversation context thus generalized for unseen entities. (2) a knowledge interpreter is proposed to model the generalized representation of an entity by the structure information of knowledge graphs and the context information of the conversation, which allows our method to generate informative responses with the unseen knowledge graph during inference. (3) earl learns the relation embeddings for conversation generation while it does not need to store representations for millions of entities, making it scalable to apply large-scale knowledge graphs. figure 1 shows conversation samples generated by earl and the prior knowledge-grounded baseline, where earl (the last line) can inject the unseen entities (the white nodes) coherently. in contrast, the prior baseline model wrongly utilizes the seen knowledge graph (the grey nodes) in conversation generation given the unseen entities as input. to summarize, our contributions are as follows: ## conclusion and future work in this paper, we present an entity-agnostic representation learning method to incorporate knowledge graphs into informative conversation generation. it learns to represent entities using the relational structure of the knowledge graph instead of parameterizing billions of entities directly, thereby more suitable for applying large-scale unseen graphs. automatic and manual evaluations show that earl can generate appropriate and in-formative responses with both seen and unseen knowledge graphs as input. in future work, we will explore the pre-trained knowledge-grounded conversation model based on earl, which can incorporate the large-scale knowledge graphs with entities in multiple hops into conversation generation.