  Task-oriented conversational modeling with unstructured knowledge access, as
track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to
build a system to generate response given dialogue history and knowledge
access. This challenge can be separated into three subtasks, (1)
knowledge-seeking turn detection, (2) knowledge selection, and (3)
knowledge-grounded response generation. We use pre-trained language models,
ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1
and 2, the coarse-grained information like domain and entity are used to
enhance knowledge usage. For subtask 3, we use a latent variable to encode
dialog history and selected knowledge better and generate responses combined
with copy mechanism. Meanwhile, some useful post-processing strategies are
performed on the model's final output to make further knowledge usage in the
generation task. As shown in released evaluation results, our proposed system
ranks second under objective metrics and ranks fourth under human metrics.
