Input-to-Output Gate to Improve RNN Language Models
2017-09-26IJCNLP 2017Code Available0· sign in to hype
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.