@inproceedings{takase-etal-2017-input,
title = "Input-to-Output Gate to Improve {RNN} Language Models",
author = "Takase, Sho and
Suzuki, Jun and
Nagata, Masaaki",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2008",
pages = "43--48",
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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<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.</abstract>
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%0 Conference Proceedings
%T Input-to-Output Gate to Improve RNN Language Models
%A Takase, Sho
%A Suzuki, Jun
%A Nagata, Masaaki
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 nov
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F takase-etal-2017-input
%X 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.
%U https://aclanthology.org/I17-2008
%P 43-48
Markdown (Informal)
[Input-to-Output Gate to Improve RNN Language Models](https://aclanthology.org/I17-2008) (Takase et al., IJCNLP 2017)
ACL
- Sho Takase, Jun Suzuki, and Masaaki Nagata. 2017. Input-to-Output Gate to Improve RNN Language Models. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 43–48, Taipei, Taiwan. Asian Federation of Natural Language Processing.