End-to-end training of neural networks is a promising approach to automatic
construction of dialog systems using a human-to-human dialog corpus. Recently,
Vinyals et al. tested neural conversation models using OpenSubtitles. Lowe et
al. released the Ubuntu Dialogue Corpus for researching unstructured multi-turn
dialogue systems. Furthermore, the approach has been extended to accomplish
task oriented dialogs to provide information properly with natural
conversation. For example, Ghazvininejad et al. proposed a knowledge grounded
neural conversation model [3], where the research is aiming at combining
conversational dialogs with task-oriented knowledge using unstructured data
such as Twitter data for conversation and Foursquare data for external
knowledge.However, the task is still limited to a restaurant information
service, and has not yet been tested with a wide variety of dialog tasks. In
addition, it is still unclear how to create intelligent dialog systems that can
respond like a human agent.
In consideration of these problems, we proposed a challenge track to the 6th
dialog system technology challenges (DSTC6) using human-to-human dialog data to
mimic human dialog behaviors. The focus of the challenge track is to train
end-to-end conversation models from human-to-human conversation and accomplish
end-to-end dialog tasks in various situations assuming a customer service, in
which a system plays a role of human agent and generates natural and
informative sentences in response to user's questions or comments given dialog
context.

本文提出基于人人对话数据的挑战赛以训练端到端对话模型，实现了端到端的对话任务，模拟了客户服务的各种情况。

DSTC6 中的端到端对话建模赛道

End-to-end Conversation Modeling Track in DSTC6

Deep latent variable models have been shown to facilitate the response
generation for open-domain dialog systems. However, these latent variables are
highly randomized, leading to uncontrollable generated responses. In this
paper, we propose a framework allowing conditional response generation based on
specific attributes. These attributes can be either manually assigned or
automatically detected. Moreover, the dialog states for both speakers are
modeled separately in order to reflect personal features. We validate this
framework on two different scenarios, where the attribute refers to genericness
and sentiment states respectively. The experiment result testified the
potential of our model, where meaningful responses can be generated in
accordance with the specified attributes.

提出了一种框架，基于特定属性实现条件式回应生成，应用于两种不同的情景，结果表明模型在生成符合指定属性的响应方面具有潜力。

一种基于条件变分框架的对话生成模型

A Conditional Variational Framework for Dialog Generation

End-to-end learning of recurrent neural networks (RNNs) is an attractive
solution for dialog systems; however, current techniques are data-intensive and
require thousands of dialogs to learn simple behaviors. We introduce Hybrid
Code Networks (HCNs), which combine an RNN with domain-specific knowledge
encoded as software and system action templates. Compared to existing
end-to-end approaches, HCNs considerably reduce the amount of training data
required, while retaining the key benefit of inferring a latent representation
of dialog state. In addition, HCNs can be optimized with supervised learning,
reinforcement learning, or a mixture of both. HCNs attain state-of-the-art
performance on the bAbI dialog dataset, and outperform two commercially
deployed customer-facing dialog systems.

Hybrid Code Networks (HCNs) combine recurrent neural networks (RNNs) with domain-specific knowledge, reducing the training data needed for dialog systems while retaining the benefit of inferring a latent representation of dialog state.

混合编码网络：具备监督和强化学习的实用高效端到端对话控制

Hybrid Code Networks: practical and efficient end-to-end dialog control  with supervised and reinforcement learning

This paper describes a new spoken dialog portal that connects systems
produced by the spoken dialog academic research community and gives them access
to real users. We introduce a distributed, multi-modal, multi-agent prototype
dialog framework that affords easy integration with various remote resources,
ranging from end-to-end dialog systems to external knowledge APIs. To date, the
DialPort portal has successfully connected to the multi-domain spoken dialog
system at Cambridge University, the NOAA (National Oceanic and Atmospheric
Administration) weather API and the Yelp API.

本文介绍了一种新的口语对话门户，与多个系统进行连接，并通过多个平台进行易于整合的分布式多模式多代理原型对话框架的介绍，其中包括终端对话系统和知识 API。 到目前为止， DialPort 门户已成功连接到剑桥大学的多域口语对话系统，NOAA 天气 API 和 Yelp API。