  In a dialog, there can be multiple valid next utterances at any point. The
present end-to-end neural methods for dialog do not take this into account.
They learn with the assumption that at any time there is only one correct next
utterance. In this work, we focus on this problem in the goal-oriented dialog
setting where there are different paths to reach a goal. We propose a new
method, that uses a combination of supervised learning and reinforcement
learning approaches to address this issue. We also propose a new and more
effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid
next utterances to the original-bAbI dialog tasks, which allows evaluation of
goal-oriented dialog systems in a more realistic setting. We show that there is
a significant drop in performance of existing end-to-end neural methods from
81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on
permuted-bAbI dialog tasks. We also show that our proposed method improves the
performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog
tasks.
