Input-to-Output Gate to Improve RNN Language Models

Sho Takase; Jun Suzuki; Masaaki Nagata

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arXiv · 1709.08907

Input-to-Output Gate to Improve RNN Language Models

Sho Takase·Jun Suzuki·Masaaki Nagata

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Abstract

This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.

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Sho Takase, Jun Suzuki, Masaaki Nagata. 2017-09-28. Input-to-Output Gate to Improve RNN Language Models. https://arxiv.org/abs/1709.08907

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