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Plain recurrent networks greatly suffer from the vanishing gradient problemwhile Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) andGated Recurrent Unit (GRU) deliver promising results in many sequence learningtasks through sophisticated network designs. This paper shows how we canaddress this problem in a plain recurrent network by analyzing the gatingmechanisms in GNNs. We propose a novel network called the Recurrent IdentityNetwork (RIN) which allows a plain recurrent network to overcome the vanishinggradient problem while training very deep models without the use of gates. Wecompare this model with IRNNs and LSTMs on multiple sequence modelingbenchmarks. The RINs demonstrate competitive performance and converge faster inall tasks. Notably, small RIN models produce 12%--67% higher accuracy on theSequential and Permuted MNIST datasets and reach state-of-the-art performanceon the bAbI question answering dataset.

Overcoming the vanishing gradient problem in plain recurrent networks