基于知识增强的开放域多轮对话模型

如何减轻安全回复和重复回复一直是开放域多轮对话模型的两大挑战性难题.然而,现有开放域对话模型往往忽略了对话目标的引导性作用,以及如何在对话历史和对话目标中引入和选择更精确的知识信息.鉴于此,提出基于知识增强的多轮对话模型.所提模型首先将对话历史中实词进行义原及领域词替换,达到消除歧义和丰富对话文本表示的效果.然后将经过知识增强后的对话历史、扩充的三元组世界知识、知识管理和知识拷贝加以集成,以融合知识、词汇、对话历史和对话目标多种信息,生成多样性回复.通过两个国际基准开放域汉语对话语料库上的实验结果及可视化验证所提模型同时在自动评测和人工评测上的有效性.

Open-domain Multi-turn Dialogue Model Based on Knowledge Enhancement

How to reduce secure and repeated replies is a challenging problem in the open-domain multi-turn dialogue model. However, the existing open-domain dialogue models often ignore the guiding role of dialogue objectives and how to introduce and select more accurate knowledge information in dialogue history and dialogue objectives. Based on these phenomena, this study proposes a multi-turn dialogue model based on knowledge enhancement. Firstly, the model replaces the notional words in the dialogue history with semaphores and domain words, so as to eliminate ambiguity and enrich the dialogue text representation. Then, the knowledge-enhanced dialogue history and expanded triplet world knowledge are effectively integrated into the knowledge management and knowledge copy modules, so as to integrate information of knowledge, vocabularies, dialogue history, and dialogue objectives and generate diverse responses. The experimental results and visualization on two international benchmark open-domain Chinese dialogue corpora verify the effectiveness of the proposed model in both automatic evaluation and human judgment.