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

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2845-2850.

Abstract

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided with 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 computers only need 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.

Xiang Li, Lili Mou, Rui Yan, and Ming Zhang. 2016. StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2845–2850. https://ijcai.org/proceedings/2016/404
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