  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.
