融合对话连贯记忆的角色一致性回复生成方法

Persona Consistency Response Generation by Integrating Dialogue Coherence Memory

现有的对话系统研究往往忽略了对话过程中的角色一致性问题,这可能会导致对话前后的角色不匹配现象,从而降低用户的对话体验,同时,对话过程中的前后连贯性问题也备受人们所关注。为了解决以上问题,提出了一种融合对话连贯记忆的角色一致性回复生成方法。通过角色扩展模块将原始数据集进行角色扩展,得到角色扩展数据集;通过数据提取模块将相关度较高的角色信息与对话历史进行提取,并从中去除冗余信息,得到训练数据集;在回复生成模块中引入角色一致记忆模块和对话连贯记忆模块,使得对话模型在对话过程中始终保持对角色信息以及对话历史的记忆特性,促使其生成对话连贯的角色一致性回复。在Persona-Chat数据集上的实验结果表明,该方法生成的回复在对话连贯性以及角色一致性方面都得到了一定的提升。

The existing research on dialogue systems often overlooks the issue of persona consistency during the dialogue process,which may lead to persona mismatches before and after the dialogue,thereby reducing the user’s dialogue experi-ence.At the same time,the issue of coherence before and after the dialogue process is also of great concern.To address the above issues,a persona consistency response generation method that integrates dialogue coherence memory is proposed.Firstly,the original dataset is persona-extended through the persona extension module to obtain the persona-extended dataset.Secondly,the highly correlated persona information and dialogue history are extracted through the data extraction module,and redundant information is removed to obtain the training dataset.Finally,the persona consistency memory module and dialogue coherence memory module are introduced in the response generation module,so that the dialogue model always maintains the memory characteristics of persona information and dialogue history during the dialogue process,promoting its generation of dialogue-coherent persona consistent response.The experimental results on the Persona-Chat dataset show that the responses generated by this method have been improved in terms of dialogue coherence and persona consistency.