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.

本文提出了一种基于动态多种知识融合的开放域聊天机器人模型，其中结构化的三元组来自知识图谱，非结构化的文本来自文档，并采用动态虚拟知识选择器和控制器以及新颖的动态知识内存模块来扩展知识空间，实验结果表明了方法的有效性。

基于动态多形式知识融合的开放域对话生成

Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion

Knowledge-based visual question answering (VQA) is a vision-language task
that requires an agent to correctly answer image-related questions using
knowledge that is not presented in the given image. It is not only a more
challenging task than regular VQA but also a vital step towards building a
general VQA system. Most existing knowledge-based VQA systems process knowledge
and image information similarly and ignore the fact that the knowledge base
(KB) contains complete information about a triplet, while the extracted image
information might be incomplete as the relations between two objects are
missing or wrongly detected. In this paper, we propose a novel model named
dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which
performs explicit and implicit reasoning over a key-value knowledge memory
module and a spatial-aware image graph, respectively. Specifically, the memory
module learns a dynamic knowledge representation and generates a
knowledge-aware question representation at each reasoning step. Then, this
representation is used to guide a graph attention operator over the
spatial-aware image graph. Our model achieves new state-of-the-art accuracy on
the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the
effectiveness of each component of the proposed model.

本文提出一种名为动态知识记忆增强多步图推理（DMMGR）的新型模型，能够在键 - 值知识记忆模块和空间感知图像图上执行显式和隐式推理，并在 KRVQR 和 FVQA 数据集上实现了新的最先进的准确性。