Recurrently Controlling a Recurrent Network with Recurrent Networks Controlled by More Recurrent Networks

Yi Tay, Yikang Shen, Alvin Chan, Aston Zhang, Shuai Zhang

Keywords: Deep Learning, Recurrent Neural Networks

Abstract: This paper explores an intriguing idea of recursively parameterizing recurrent nets. Simply speaking, this refers to recurrently controlling a recurrent network with recurrent networks controlled by recurrent networks. The proposed architecture recursively parameterizes its gating functions whereby gating mechanisms of X-RNN are controlled by instances of itself, which are repeatedly called in a recursive fashion. We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data. To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving optimistic and competitive results on all tasks.

One-sentence Summary: Having fun with RNNs

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