  We introduce a neural network with a recurrent attention model over a
possibly large external memory. The architecture is a form of Memory Network
(Weston et al., 2015) but unlike the model in that work, it is trained
end-to-end, and hence requires significantly less supervision during training,
making it more generally applicable in realistic settings. It can also be seen
as an extension of RNNsearch to the case where multiple computational steps
(hops) are performed per output symbol. The flexibility of the model allows us
to apply it to tasks as diverse as (synthetic) question answering and to
language modeling. For the former our approach is competitive with Memory
Networks, but with less supervision. For the latter, on the Penn TreeBank and
Text8 datasets our approach demonstrates comparable performance to RNNs and
LSTMs. In both cases we show that the key concept of multiple computational
hops yields improved results.
