Learning Goal-oriented Dialog using Gated End-to-End Memory Networks

In this paper, we introduce a novel memory network model using an end-to-end differentiable memory access regulation mechanism. It is inspired by the current progress on the connection short-cutting principle in the field of computer vision. We name it Gated End-to-End Memory Network (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show improvements on all of the Dialog bAbI tasks, particularly on the real human-bot conversion-based Dialog State Tracking Challenge (DSTC2) dataset. This method does not require the use of any domain knowledge. Our model sets a new state of the art of end-to-end trainable dialog systems on this dataset.