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In this paper, we introduce a novel type of Rectified Linear Unit (ReLU),called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with anunbounded positive and negative image, can be used as a drop-in replacement fora tanh activation function in the recurrent step of Quasi-Recurrent NeuralNetworks (QRNNs) (Bradbury et al. (2017)). Similar to ReLUs, DReLUs are lessprone to the vanishing gradient problem, they are noise robust, and they inducesparse activations.  We independently reproduce the QRNN experiments of Bradbury et al. (2017) andcompare our DReLU-based QRNNs with the original tanh-based QRNNs and LongShort-Term Memory networks (LSTMs) on sentiment classification and word-levellanguage modeling. Additionally, we evaluate on character-level languagemodeling, showing that we are able to stack up to eight QRNN layers withDReLUs, thus making it possible to improve the current state-of-the-art incharacter-level language modeling over shallow architectures based on LSTMs.

Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks