End-To-End Memory Networks
This work addresses the need for more generally applicable memory networks in AI by reducing training supervision, though it is incremental as it builds on existing Memory Networks and RNNsearch.
The paper tackles the problem of reducing supervision in memory-augmented neural networks by introducing an end-to-end trainable model with recurrent attention over external memory, achieving competitive performance on question answering with less supervision and comparable results on language modeling tasks.
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.