基于情感变量的二阶段对话生成模型

情感对话生成是近年来自然语言处理任务中的热门方向之一,生成带有情感色彩的响应能提高人机间的互动性。现有的情感对话生成模型情感变量单一,容易生成枯燥的响应。为确保响应语句不仅语义逻辑正确且具有多样性,该文提出了二阶段对话生成模型。第一阶段,利用DialoGPT强大的语言理解能力来确保生成语义正确的响应;为解决响应枯燥单调的缺点,该文提出融合主情感变量和混合情感变量作为全局情感变量用于后续操作;第二阶段,在第一阶段生成的响应基础上,利用全局情感变量对语句进行重写操作,从而生成高质量的响应。实验结果表明,该文提出的模型在Empathetic Dialogues数据集上的响应质量要优于基线模型。

A Two-Stage Dialogue Generation Model Based on Affective Variables

Emotional dialogue generation has become one of the popular topics in natural language processing. It can improve the interaction between human and computer, but existing affective dialogue generation models only use a single affective variable and is easy to generate boring responses. To ensure the response sentences are not only semantically correct but also diversified, a two-stage dialogue generation model is proposed in this paper. In the first stage, DialoGPT with its powerful language understanding capabilities are used to ensure that responses with correct semantics can be generated. Main emotional variables and mixed emotional variables are fused to be global emotional variables to deal with the boring response. In the second stage, the global emotional variable is used to rewrite the response generated in the first stage, so as to polish the statement. Experimental results show that the proposed model performs better on the Empathetic Dialogues dataset than the baseline models.