  We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an
agent can guess the target document that the user is interested in by leading a
dialogue. To benchmark progress, we introduce a new dataset of GuessMovie,
which contains 16,881 documents, each describing a movie, and associated 13,434
dialogues. Further, we propose the MD3 model. Keeping guessing the target
document in mind, it converses with the user conditioned on both document
engagement and user feedback. In order to incorporate large-scale external
documents into the dialogue, it pretrains a document representation which is
sensitive to attributes it talks about an object. Then it tracks dialogue state
by detecting evolvement of document belief and attribute belief, and finally
optimizes dialogue policy in principle of entropy decreasing and reward
increasing, which is expected to successfully guess the user's target in a
minimum number of turns. Experiments show that our method significantly
outperforms several strong baseline methods and is very close to human's
performance.
