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

Xiang Li, Lili Mou, Rui Yan, Ming Zhang
<span title="2016-04-15">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... 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.
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