Non-goal oriented, generative dialogue systems lack the ability to generate
answers with grounded facts. A knowledge graph can be considered an abstraction
of the real world consisting of well-grounded facts. This paper addresses the
problem of generating well grounded responses by integrating knowledge graphs
into the dialogue systems response generation process, in an end-to-end manner.
A dataset for nongoal oriented dialogues is proposed in this paper in the
domain of soccer, conversing on different clubs and national teams along with a
knowledge graph for each of these teams. A novel neural network architecture is
also proposed as a baseline on this dataset, which can integrate knowledge
graphs into the response generation process, producing well articulated,
knowledge grounded responses. Empirical evidence suggests that the proposed
model performs better than other state-of-the-art models for knowledge graph
integrated dialogue systems.

该论文探讨了将知识图谱集成到对话系统响应生成过程中的问题，提出了一种以足球为领域的非目标导向对话数据集和每个团队的知识图谱，并提出了一种新的神经网络架构作为基线，在数据集上集成知识图谱，产生知识化的响应，经实验证明，该模型的表现优于目前已有的知识图谱集成对话系统的最新模型。

使用 KG-Copy 网络进行非目标导向型对话

Using a KG-Copy Network for Non-Goal Oriented Dialogues

We introduce a new approach to generative data-driven dialogue systems (e.g.
chatbots) called TransferTransfo which is a combination of a Transfer learning
based training scheme and a high-capacity Transformer model. Fine-tuning is
performed by using a multi-task objective which combines several unsupervised
prediction tasks. The resulting fine-tuned model shows strong improvements over
the current state-of-the-art end-to-end conversational models like memory
augmented seq2seq and information-retrieval models. On the privately held
PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this
approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and
F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute
improvement) and 19.5 (20 % absolute improvement).

该研究提出了一种新的生成数据驱动对话系统的方法，称为 TransferTransfo，它是基于传输学习和高容量 Transformer 模型的组合，使用多任务目标进行微调，取得了当前最先进的端到端对话模型如记忆增强 seq2seq 和信息检索模型所不能比拟的强大改进。

TransferTransfo: 基于神经网络的对话代理的转移学习方法

TransferTransfo: A Transfer Learning Approach for Neural Network Based  Conversational Agents

Using neural networks to generate replies in human-computer dialogue systems
is attracting increasing attention over the past few years. However, the
performance is not satisfactory: the neural network tends to generate safe,
universally relevant replies which carry little meaning. In this paper, we
propose a content-introducing approach to neural network-based generative
dialogue systems. We first use pointwise mutual information (PMI) to predict a
noun as a keyword, reflecting the main gist of the reply. We then propose
seq2BF, a "sequence to backward and forward sequences" model, which generates a
reply containing the given keyword. Experimental results show that our approach
significantly outperforms traditional sequence-to-sequence models in terms of
human evaluation and the entropy measure, and that the predicted keyword can
appear at an appropriate position in the reply.

本文提出了一种基于内容引入的神经网络生成式对话系统方法，使用点间互信息预测名词作为关键词，并采用 seq2BF 模型生成包含关键字的回答，该方法在人类评估和熵测量方面明显优于传统的序列到序列方法。