Research on Dialogue Generation Algorithm Based on Explicit Weighted Context

Abstract

At present, generating responses based on neural network learning has become a hot spot, and has gradually entered a new stage of pre-training language model. The intelligence and transferability of the dialogue system in the open domain is becoming more and more obvious, but there are still many problems, such as single reply, logic contradiction, general security answer. So as to solve the weakness in reply, we starting from the context, this paper uses the method of Point Mutual Information (PMI)to calculate the relevant weight between the context and the current dialogue to explicitly weight the context and give full play to the effective information of the current dialogue. Further inputing into the dialogue generation of the pre-training language model for fine-tuning. In the experimental evaluation, we make a comprehensive analysis from three aspects: automatic evaluation, objective index calculation and manual evaluation. The results show that our method of explicit weighted context coding will enrich the coding information further generating more diverse and meaningful responses, can be significantly improved compared with the baseline model.