多通道自然人机对话系统要求计算机能够对用户的语句产生智能应答,传统的人机对话系统由于知识库的限制以及用户话语的随意性,当对话内容超出知识库范围时,系统将无法应答或产生与用户期望不符的回答,这在一定程度上影响了人机对话系统用户的体验感.为了解决该问题,提出了一种融合多模态历史交互信息和面向数据的句法分析(data-oriented parsing,简称DOP)模型的最优答句生成方法:首先从大规模句法树库中提取上下文无关文法的语法规则,然后结合对话过程中用户呈现的表情、姿态等多模态历史交互信息,融合DOP模型对上下文无关文法生成的汉语句子进行过滤,最终生成一个符合语法规则且符合语义的答句返回给用户,让计算机在无法获得知识库支撑时,根据交互历史信息生成应对当前对话的语句,有效地提升了多通道自然人机交互系统用户的体验感.该方法应用于交通信息查询以及咖啡厅的多主题多模态人机自由对话系统.用户的体验表明,该方法能够有效提高用户交互的自然度和体验感.

Natural multimodal human computer interaction dialog requires computer be able to produce intelligent response to user's statement. Due to the limitations of knowledge base and randomness of user's discourse, a traditional human-computer dialogue system cannot answer or produce consistent answer with user's expectations when the conversation is beyond the scope of knowledge, thus affecting user's sense of experience to the natural machine dialogue system. To solve this problem, this paper presents a method of generating optimal sentence by integrating multi-modal interaction history information and data-oriented parsing model. First, rules of context-free grammar from large-scale syntax tree libraries are extracted. Then combining user's expressions, gestures and other multi-modal interaction history information in dialogue process, a data-oriented parsing (DOP) model is integrated to filter Chinese sentences which are generated by context-free grammars, ultimately generating a sentence which is grammatically and semantically sound. The method allows a computer to generate responses to the current dialogue according to the interaction history information when the system can't get the support of knowledge base, therefore enhancing user's experience to multi-channel natural-machine interaction system. The proposed method is applied to traffic information search and multi-modal multi-topic dialogue system, and the result shows it can effectively improve the naturalness and enhance user's experience.

国家自然科学基金(61273288, 61233009, 61203258, 61305003, 61332017, 61375027);国家社会科学基金(13&ZD189)