  Recent empirical results on long-term dependency tasks have shown that neural
networks augmented with an external memory can learn the long-term dependency
tasks more easily and achieve better generalization than vanilla recurrent
neural networks (RNN). We suggest that memory augmented neural networks can
reduce the effects of vanishing gradients by creating shortcut (or wormhole)
connections. Based on this observation, we propose a novel memory augmented
neural network model called TARDIS (Temporal Automatic Relation Discovery in
Sequences). The controller of TARDIS can store a selective set of embeddings of
its own previous hidden states into an external memory and revisit them as and
when needed. For TARDIS, memory acts as a storage for wormhole connections to
the past to propagate the gradients more effectively and it helps to learn the
temporal dependencies. The memory structure of TARDIS has similarities to both
Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but
both read and write operations of TARDIS are simpler and more efficient. We use
discrete addressing for read/write operations which helps to substantially to
reduce the vanishing gradient problem with very long sequences. Read and write
operations in TARDIS are tied with a heuristic once the memory becomes full,
and this makes the learning problem simpler when compared to NTM or D-NTM type
of architectures. We provide a detailed analysis on the gradient propagation in
general for MANNs. We evaluate our models on different long-term dependency
tasks and report competitive results in all of them.
