Open-domain multi-turn conversations normally face the challenges of how to enrich and expand the content of the conversation. Recently, many approaches based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph, this paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatt-ing Machine (DMKCM).In particular, DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively uses historical reasoning knowledge to generate better responses. Experimental results indicate the effectiveness of our method in terms of dialogue coherence and informativeness.
翻译:开放式多方向对话通常面临如何丰富和扩大对话内容的挑战。最近,提出了许多基于外部知识的多种方法,以产生丰富的语义和信息对话。研究了两种类型的知识,以生成有知识意识的开放域对话:由知识图和文件的无结构文本组成的三重结构。要利用文件中丰富的无结构潜在知识以及结构化知识图的信息扩展能力的优势,本文件将展示一个新的对话生成模型,动态多形式知识组合模式,以开放多端聊天机为基础。特别是,DMKCM应用了一种索引化文本(虚拟知识库),将相关文件定位为第1跳,然后扩大对话的内容,并开始使用共同知识图将相容的三重知识作为第二跳。要将这两种知识形式有效地纳入对话,我们设计了一种动态虚拟知识选择器和一个控制器,帮助丰富和扩大知识空间。此外,DMKCM采用一个新型的动态知识存储模块,有效地利用历史推理学一致性和更好反应的方法。