  Grounding human-machine conversation in a document is an effective way to
improve the performance of retrieval-based chatbots. However, only a part of
the document content may be relevant to help select the appropriate response at
a round. It is thus crucial to select the part of document content relevant to
the current conversation context. In this paper, we propose a document content
selection network (CSN) to perform explicit selection of relevant document
contents, and filter out the irrelevant parts. We show in experiments on two
public document-grounded conversation datasets that CSN can effectively help
select the relevant document contents to the conversation context, and it
produces better results than the state-of-the-art approaches. Our code and
datasets are available at https://github.com/DaoD/CSN.
