Emotion Elicited Question Generation Model in Dialogue Scenarios
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 emo-tional 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.
万方数据
维普
