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Although the Conditional Variational AutoEncoder (CVAE) model can generatemore diversified responses than the traditional Seq2Seq model, the responsesoften have low relevance with the input words or are illogical with thequestion. A causal analysis is carried out to study the reasons behind, and amethodology of searching for the mediators and mitigating the confounding biasin dialogues is provided. Specifically, we propose to predict the mediators topreserve relevant information and auto-regressively incorporate the mediatorsinto generating process. Besides, a dynamic topic graph guided conditionalvariational autoencoder (TGG-CVAE) model is utilized to complement the semanticspace and reduce the confounding bias in responses. Extensive experimentsdemonstrate that the proposed model is able to generate both relevant andinformative responses, and outperforms the state-of-the-art in terms ofautomatic metrics and human evaluations.

Incorporating Causal Analysis into Diversified and Logical Response Generation