StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation Xiang Li,1 Lili Mou,1 Rui Yan,2 Ming Zhang1 1School of EECS, Peking University, China 2Natural Language Processing Department, Baidu Inc., China

Human-computer Conversation One of the most challenging problems in artificial intelligence The computer either searches or synthesizes a reply given an utterance (called query) issued by a user Industrial products like Siri of Apple, Xiaobing of Microsoft, and Dumi of Baidu, etc.

Passive or Proactive Passive: To “respond” only Proactive: To introduce new content when stalemate occurs Human-human conversation statistics:

Mixed-initiative Systems in Vertical VS Open Domains Vertical domain Train95, AutoTutor, etc. Feasible to manually design rules and templates The content to be introduced is nearly certain Open-domain Users are free to say anything Impossible to specify rules/templates in advance

Contributions The first to address the problem of content introducing in open-domain conversation systems A complete pipeline, involving when, what, and how to introduce new content The Bi-PageRank-HITS reranking algorithm, emphasizing rich interaction between conversation context and candidate replies

Architecture Stalemate detection Named entity detection Keyword filters like “Err”, “Errr”, etc. Named entity detection Named entities reflect users' interest Candidate reply retrieval Retrieve candidate replies by entities & conversation context Selection by reranking Candidate replies are reranked by a random walk-like algorithm

Process Flow The system is built upon a conventional retrieval-based conversation system, which is typically passive

Reranking Algorithm Importance of each query or reply, interaction between queries and replies Bi-PageRank-HITS: combination of PageRank and HITS Formulate queries and replies as a bipartite graph Alternate between the following two steps PageRank: Rank one side in the bipartite graph HITS: Propagate information to the other side

Reranking Algorithm PageRank step: [·]: column normalization M: similarity matrix of either queries or replies x, y: prior distributions over queries and replies, uniformly initialized and updated after HITS HITS step: x: query scores, y: reply scores (see also PageRank) Weight matrix is updated according to PageRank scores, given by where φ(·,·) is a static, text-based relevance score

Reranking Algorithm Global iteration over PageRank and HITS

Evaluation Dataset: sessions from real-world user conversation logs Human evaluation: 1 point = good; 0 point = not good

Analysis Parameter: textual information for queries does recommend important queries. On the contrary, textual information for replies is inimical. Convergence: for the 10 randomly chosen samples, they typically converge quickly in 3--5 global iterations.

Case Study

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