  Machine reading using differentiable reasoning models has recently shown
remarkable progress. In this context, End-to-End trainable Memory Networks,
MemN2N, have demonstrated promising performance on simple natural language
based reasoning tasks such as factual reasoning and basic deduction. However,
other tasks, namely multi-fact question-answering, positional reasoning or
dialog related tasks, remain challenging particularly due to the necessity of
more complex interactions between the memory and controller modules composing
this family of models. In this paper, we introduce a novel end-to-end memory
access regulation mechanism inspired by the current progress on the connection
short-cutting principle in the field of computer vision. Concretely, we develop
a Gated End-to-End trainable Memory Network architecture, 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 significant
improvements on the most challenging tasks in the 20 bAbI dataset, without the
use of any domain knowledge. Then, we show improvements on the dialog bAbI
tasks including the real human-bot conversion-based Dialog State Tracking
Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state
of the art.
