  Recently, topic-grounded dialogue system has attracted significant attention
due to its effectiveness in predicting the next topic to yield better responses
via the historical context and given topic sequence. However, almost all
existing topic prediction solutions focus on only the current conversation and
corresponding topic sequence to predict the next conversation topic, without
exploiting other topic-guided conversations which may contain relevant
topic-transitions to current conversation. To address the problem, in this
paper we propose a novel approach, named Sequential Global Topic Attention
(SGTA) to exploit topic transition over all conversations in a subtle way for
better modeling post-to-response topic-transition and guiding the response
generation to the current conversation. Specifically, we introduce a latent
space modeled as a Multivariate Skew-Normal distribution with hybrid kernel
functions to flexibly integrate the global-level information with
sequence-level information, and predict the topic based on the distribution
sampling results. We also leverage a topic-aware prior-posterior approach for
secondary selection of predicted topics, which is utilized to optimize the
response generation task. Extensive experiments demonstrate that our model
outperforms competitive baselines on prediction and generation tasks.
