This paper introduces a document grounded dataset for text conversations. We
define "Document Grounded Conversations" as conversations that are about the
contents of a specified document. In this dataset the specified documents were
Wikipedia articles about popular movies. The dataset contains 4112
conversations with an average of 21.43 turns per conversation. This positions
this dataset to not only provide a relevant chat history while generating
responses but also provide a source of information that the models could use.
We describe two neural architectures that provide benchmark performance on the
task of generating the next response. We also evaluate our models for
engagement and fluency, and find that the information from the document helps
in generating more engaging and fluent responses.