  Although the Conditional Variational AutoEncoder (CVAE) model can generate
more diversified responses than the traditional Seq2Seq model, the responses
often have low relevance with the input words or are illogical with the
question. A causal analysis is carried out to study the reasons behind, and a
methodology of searching for the mediators and mitigating the confounding bias
in dialogues is provided. Specifically, we propose to predict the mediators to
preserve relevant information and auto-regressively incorporate the mediators
into generating process. Besides, a dynamic topic graph guided conditional
variational autoencoder (TGG-CVAE) model is utilized to complement the semantic
space and reduce the confounding bias in responses. Extensive experiments
demonstrate that the proposed model is able to generate both relevant and
informative responses, and outperforms the state-of-the-art in terms of
automatic metrics and human evaluations.

Computation and Language;Artificial Intelligence