  Existing open-domain human-computer conversation systems are typically
passive: they either synthesize or retrieve a reply provided a human-issued
utterance. It is generally presumed that humans should take the role to lead
the conversation and introduce new content when a stalemate occurs, and that
the computer only needs to "respond." In this paper, we propose
StalemateBreaker, a conversation system that can proactively introduce new
content when appropriate. We design a pipeline to determine when, what, and how
to introduce new content during human-computer conversation. We further propose
a novel reranking algorithm Bi-PageRank-HITS to enable rich interaction between
conversation context and candidate replies. Experiments show that both the
content-introducing approach and the reranking algorithm are effective. Our
full StalemateBreaker model outperforms a state-of-the-practice conversation
system by +14.4% p@1 when a stalemate occurs.
