  The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of rules. However, incorporating personalization into such systems
is a largely unexplored topic as there are no existing corpora to facilitate
such work. In this paper, we present a new dataset of goal-oriented dialogs
which are influenced by speaker profiles attached to them. We analyze the
shortcomings of an existing end-to-end dialog system based on Memory Networks
and propose modifications to the architecture which enable personalization. We
also investigate personalization in dialog as a multi-task learning problem,
and show that a single model which shares features among various profiles
outperforms separate models for each profile.
