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

StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation