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

TL;DR

This paper introduces StalemateBreaker, a proactive conversation system that can introduce new content during human-computer dialogue, improving response relevance and overcoming conversational stalemates.

Contribution

It presents a novel pipeline for proactive content introduction and a reranking algorithm Bi-PageRank-HITS, enhancing dialogue systems' ability to handle stalemates.

Findings

Outperforms existing systems by +14.4% p@1 during stalemates.

Effective content introduction improves conversation flow.

Bi-PageRank-HITS enhances reply relevance through rich context interaction.

Abstract

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.