  We present our work on Track 2 in the Dialog System Technology Challenges 7
(DSTC7). The DSTC7-Track 2 aims to evaluate the response generation of fully
data-driven conversation models in knowledge-grounded settings, which provides
the contextual-relevant factual texts. The Sequenceto-Sequence models have been
widely used for end-to-end generative conversation modelling and achieved
impressive results. However, they tend to output dull and repeated responses in
previous studies. Our work aims to promote the diversity for end-to-end
conversation response generation, which follows a two-stage pipeline: 1)
Generate multiple responses. At this stage, two different models are proposed,
i.e., a variational generative (VariGen) model and a retrieval based
(Retrieval) model. 2) Rank and return the most related response by training a
topic coherence discrimination (TCD) model for the ranking process. According
to the official evaluation results, our proposed Retrieval and VariGen systems
ranked first and second respectively on objective diversity metrics, i.e.,
Entropy, among all participant systems. And the VariGen system ranked second on
NIST and METEOR metrics.
