---
title: "StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation"
authors:
  - "Xiang Li"
  - "Lili Mou"
  - "Rui Yan"
  - "Ming Zhang"
doi: ""
url: "https://ijcai.org/proceedings/2016/404"
pdf: "https://ijcai.org/proceedings/2016/0404.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2845-2850"
track: ""
keywords:
---

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

Xiang Li, Lili Mou, Rui Yan, Ming Zhang

*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.