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.

本文研究了 Conditional Variational AutoEncoder（CVAE）生成回答时常常与原问题不相关或缺乏逻辑性的原因，并提出了一种寻找中介变量和缓解对话中混淆偏差的方法。在此基础上，提出了动态话题图引导的条件变分自动编码器（TGG-CVAE）模型，用于补充语义空间并减少响应中混淆偏差。实验证明，所提出的模型能够生成相关且信息量丰富的回答，并在自动评估和人类评估方面优于现有的最先进模型。