Enhancing Coherence and Interestingness in Knowledge-Grounded Dialogue Generation (2025)

Abstract: Open-domain dialogue systems have been increasingly applied in various situations, with a growing need to improve user engagement. One effective approach is to generate responses based on interesting external knowledge using knowledge-grounded response generation models. However, relying solely on interestingness can lead to incoherent responses, potentially diminishing user engagement. This paper proposes a novel method for generating engaging responses while maintaining contextual coherence. Our approach leverages a pre-trained knowledge-grounded response generation model and modifies the knowledge selection process to enhance response coherence and interestingness without requiring additional training. First, knowledge candidates with high contextual relevance are retrieved. These candidates are then reranked based on their interestingness and used to generate the responses. Finally, the method detects dialogue breakdowns and regenerates responses as necessary to ensure coherence. We conducted experiments using the Wizard of Wikipedia dataset and two state-of-the-art response generation models. The results indicate that the proposed method improves both response coherence and interestingness.

## introduction in recent years, significant advancements in language models have led to the widespread use of open-domain dialogue systems across various social settings. consequently, user expectations have risen, particularly regarding interactive experiences and engagement. to meet this demand, research has increasingly focused on generating responses that enhance user engagement, including empathetic, personality-based, and knowledge-grounded responses @xcite . owing to this objective, knowledge-grounded response generation models have been attracting considerable attention. these models retrieve relevant knowledge from external knowledge bases and generate responses accordingly. these models can generate diverse and informative responses @xcite . typically, these models select the most suitable knowledge as the next response from the externally given knowledge. however, such models are trained to treat only one knowledge as the correct answer, even when multiple suitable knowledges are available. consequently, they often generate mundane responses that contain well-known information, as shown in figure 1 (a). such responses often make the conversation boring for users and make it difficult to capture and maintain user interest, ultimately leading to a decrease in engagement. this study addresses the challenge of building an engaging dialogue system that captures user interest through captivating responses. several studies have demonstrated that incorporating interesting facts can effectively enhance user engagement in tasks like entity search and conversation. for instance, @xcite improved users' overall experience by augmenting entity search results with relevant, interesting information. leveraging interesting facts to capture user attention is considered an effective approach for building an engaging dialogue system. several methods have been proposed to incorporate pre-collected interesting facts into open-domain or task-oriented dialogues @xcite . however, user engagement can be significantly diminished even if the responses themselves are interesting, due to incoherence, as illustrated in figure 1 (b ). therefore, to improve user satisfaction and build an engaging dialogue system, generating responses that balance both coherence and interestingness is crucial, as depicted in figure 1 (c ). this study proposes a method for selecting knowledge based on both its relevance to the dialogue and its interestingness, allowing us the generation of responses that facilitate coherence and interest. this approach builds on a pretrained knowledge-grounded response generation model, using it as the base model while modifying the knowledge selection process during inference. first, to maintain response coherence, knowledges are filtered according to their relevance to the dialogue context. subsequently, the knowledge candidates are reranked by interestingness, assigning a quantitative score using a large language model (llm). finally, dialogue breakdown detection is applied to the generated responses using an llm to ensure response coherence. this method can be employed without the additional training of the base model and can be applied to various knowledgegrounded response generation models. our contributions can be summarized as follows: ## method we propose a method for selecting knowledge based on both its relevance to the dialogue and its interestingness, generating responses that embody both coherence and interestingness to build an engaging dialogue system. figure 2 presents an overview of the proposed method. we build upon pre-trained knowledge-grounded response generation models, using them as base models. in these base models, each knowledge is assigned a score that represents its suitability for the next response. the knowledge with the highest score is selected, and a response is generated based on that knowledge and the dialogue context. this score reflects both the appropriateness of the knowledge for the next response and its relevance to the dialogue context, which we refer to this score as the contextual relevance score. our proposed method modifies the knowledge selection process during the inference of the base models to improve response coherence and interestingness. instead of the base model's knowledge selection process, we introduce a three-step approach encompassing knowledge filtering, knowledge reranking, and dialogue breakdown detection. in the knowledge filtering step, we select knowledge candidates based on contextual relevance to ensure coherence. in the knowledge reranking step, we reorder the candidates by their interestingness to enhance engagement. finally, in the dialogue breakdown detection step, we assess the generated responses for coherence and regenerate them as needed. importantly, our method does not require additional model training and can be applied broadly to various knowledge-grounded response generation models. ## experiments we conduct experiments to compare the response generation performance of previous knowledgegrounded response generation models with and without the proposed method.