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    <titleInfo>
        <title>Universal Language Model Fine-tuning for Text Classification</title>
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    <name type="personal">
        <namePart type="given">Jeremy</namePart>
        <namePart type="family">Howard</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.</abstract>
    <identifier type="citekey">howard-ruder-2018-universal</identifier>
    <identifier type="doi">10.18653/v1/P18-1031</identifier>
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        <url>https://aclanthology.org/P18-1031</url>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Universal Language Model Fine-tuning for Text Classification
%A Howard, Jeremy
%A Ruder, Sebastian
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F howard-ruder-2018-universal
%X Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.
%R 10.18653/v1/P18-1031
%U https://aclanthology.org/P18-1031
%U https://doi.org/10.18653/v1/P18-1031
%P 328-339

Markdown (Informal)

[Universal Language Model Fine-tuning for Text Classification](https://aclanthology.org/P18-1031) (Howard & Ruder, ACL 2018)

• Universal Language Model Fine-tuning for Text Classification (Howard & Ruder, ACL 2018)

ACL

• Jeremy Howard and Sebastian Ruder. 2018. Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 328–339, Melbourne, Australia. Association for Computational Linguistics.