对话场景下的情感引导问题生成模型  

Emotion Elicited Question Generation Model in Dialogue Scenarios

作　　者：胥备 许鹏 XU Bei;XU Peng(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Jiangsu Key Laboratory of Big Data Security&Intelligent Processing,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院、软件学院、网络空间安全学院,南京210023 [2]江苏大数据安全与智能处理重点实验室,南京210023

出　　处：《计算机科学》2024年第11期265-272,共8页Computer Science

基　　金：江苏省高校自然科学基金面上项目(21KJB520017)。

摘　　要：人机对话系统已在多种智能服务场景中得到广泛应用。当前的人机对话系统可以感知对话者的情感,并根据上下文给出具备特定情感的响应。但是,具备特定情感的响应难以确保能够有效地引导人们产生特定的情感,例如,一个具备“高兴”情感的响应并不能保证人们产生高兴的情感。在一些场景中,人机对话系统需要引导用户达到某种特定的情感状态,以利于对话的持续开展或提升交互效率,如对话心理陪护或在线智能教学。当前的人机对话系统仅针对“积极/消极”等粗粒度情感引导进行了探索,难以应对细粒度情感引导任务。同时,针对对话的心理研究指出,“问题”会显著影响对话方情感的走向。基于上述背景,提出了一种对话场景下的情感引导问题生成模型。该模型基于GPT预训练模型,将需要引导对话方产生的情感作为情感知识引入模型的响应生成过程之中,同时引入了上下文情感感知机制和常识知识融合机制,并采用多任务学习的方法增强了模型的情感感知能力和对话响应生成能力。鉴于这是首次提出面向细粒度情感引导的问题生成任务,因此构建了情感引导数据集用于训练和实验,并且提出了基于提示学习的自动评价方法。最终,自动评价和人工评价的结果表明,所提模型能有效地生成问题,以引导对话方产生特定的情感。Human-machine dialog systems have been widely used in intelligent services.Existing human-machine dialog systems can perceive the interlocutor's emotional state and give a response with an appropriate emotion based on context.However,it is difficult to ensure that a response with a specific emotion can elicit the same emotion from people.For example,a response with a“joy”emotion does not guarantee that people will experience a“joy”emotion.In some scenarios,human-machine dialogue systems need to guide users to reach a specific emotional state to facilitate the continuous development of a conversation or improve inter-action efficiency,such as dialogue psychological escort or online intelligent teaching.Current human-computer dialogue systems focus on coarse-grained emotion eliciting,such as“positive/negative”,and therefore are difficult to handle fine-grained emotion eliciting.On the other side,research on dialogue psychology indicates that“questions”in a conversation can significantly affect the emotions of interlocutors.Based on the above background,a question-generation model for emotional elicitation in dialogue scenarios is proposed.This model is based on the GPT pre-trained model and incorporates the knowledge of the emotion to be elicited into the response generation.The model also introduces a contextual emotional perception mechanism and a common sense knowledge fusion mechanism and uses multi-task learning to enhance the emotion perception ability and conversation response generation ability.Given that it is the first time to propose a question generation task for fine-grained emotion eliciting,an emotional eliciting dataset has been constructed for training and experiments.An automatic evaluation method based on prompt lear-ning has been designed.Finally,automatic evaluation and human evaluation demonstrate that the proposed model can generate questions that can effectively elicit target emotions.

关 键 词：情感引导 问题生成 情感对话 提示学习 多任务学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]