SOTAVerified

Input-to-Output Gate to Improve RNN Language Models

2017-09-26IJCNLP 2017Code Available0· sign in to hype

Sho Takase, Jun Suzuki, Masaaki Nagata

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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.

Reproductions