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        <title>Input-to-Output Gate to Improve RNN Language Models</title>
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    <name type="personal">
        <namePart type="given">Sho</namePart>
        <namePart type="family">Takase</namePart>
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        <namePart type="given">Jun</namePart>
        <namePart type="family">Suzuki</namePart>
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    <name type="personal">
        <namePart type="given">Masaaki</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
            <namePart type="given">Taro</namePart>
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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
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%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)

• Input-to-Output Gate to Improve RNN Language Models (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.