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    <titleInfo>
        <title>Linear Classifier: An Often-Forgotten Baseline for Text Classification</title>
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    <name type="personal">
        <namePart type="given">Yu-Chen</namePart>
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        <namePart type="given">Si-An</namePart>
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    <name type="personal">
        <namePart type="given">Jie-Jyun</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Chih-Jen</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Anna</namePart>
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            <namePart type="family">Okazaki</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points.</abstract>
    <identifier type="citekey">lin-etal-2023-linear</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-short.160</identifier>
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        <date>2023-07</date>
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            <start>1876</start>
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%0 Conference Proceedings
%T Linear Classifier: An Often-Forgotten Baseline for Text Classification
%A Lin, Yu-Chen
%A Chen, Si-An
%A Liu, Jie-Jyun
%A Lin, Chih-Jen
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lin-etal-2023-linear
%X Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points.
%R 10.18653/v1/2023.acl-short.160
%U https://aclanthology.org/2023.acl-short.160/
%U https://doi.org/10.18653/v1/2023.acl-short.160
%P 1876-1888

Markdown (Informal)

[Linear Classifier: An Often-Forgotten Baseline for Text Classification](https://aclanthology.org/2023.acl-short.160/) (Lin et al., ACL 2023)

• Linear Classifier: An Often-Forgotten Baseline for Text Classification (Lin et al., ACL 2023)

ACL

• Yu-Chen Lin, Si-An Chen, Jie-Jyun Liu, and Chih-Jen Lin. 2023. Linear Classifier: An Often-Forgotten Baseline for Text Classification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1876–1888, Toronto, Canada. Association for Computational Linguistics.