冯广敬,刘箴,刘婷婷,许根,庄寅,王媛怡,柴艳杰. 基于情感变量的二阶段对话生成模型[J]. 中文信息学报, 2022, 36(5): 102-111.
FENG Guangjing, LIU Zhen, LIU Tingting, XU Gen, ZHUANG Yin, WANG Yuanyi, CHAI Yanjie. A Two-Stage Dialogue Generation Model Based on Affective Variables. , 2022, 36(5): 102-111.
A Two-Stage Dialogue Generation Model Based on Affective Variables
FENG Guangjing1, LIU Zhen1, LIU Tingting2, XU Gen3, ZHUANG Yin1, WANG Yuanyi2, CHAI Yanjie1
1.Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China; 2.Faculty of Information Engineering,College of Science and Technology Ningbo University, Cixi, Zhejiang 315300, China; 3.Institute of Advanced Manufacturing Technology, Ningbo Institute of Materials Technology & Engineering, CAS, Ningbo, Zhejiang 315201, China
Abstract：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.
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2022, Vol. 36 
