Personalized Dialogue Response Generation Combined with Conversation State Information
Despite the significant achievements in personalized response generation models,existing studies have not adequately considered the impact of dialogue state information on personalized dialogue responses.To address this issue,this paper proposes a self-supervised dialogue response generation model that incorporates dialogue state to effectively generate personalized replies based on pre-trained generative models.Firstly,we integrate the dialogue state into a situational comedy dataset to enhance the model's contextual understanding.Secondly,we employ self-supervised training techniques to imbue the pre-trained language ge-neration model with unique dialogue text features and employ various masking strategies to combine dialogue text and dialogue state,further enhancing model performance.Lastly,leveraging historical dialogues,we utilize the self-supervised generative model to produce personalized responses.Experimental results on a self-collected situational comedy dataset demonstrate that the dia-logue response generation model incorporating dialogue state outperforms several strong baselines across multiple metrics,thus validating the effectiveness of incorporating dialogue state in personalized response generation models.
NETL
NSTL
万方数据
