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        <title>Unsupervised Contextualized Document Representation</title>
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            <title>Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing</title>
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            <namePart type="given">Nafise</namePart>
            <namePart type="given">Sadat</namePart>
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        <name type="personal">
            <namePart type="given">Angela</namePart>
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        <name type="personal">
            <namePart type="given">Thomas</namePart>
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    <abstract>Several NLP tasks need the effective repre-sentation of text documents.Arora et al.,2017 demonstrate that simple weighted aver-aging of word vectors frequently outperformsneural models. SCDV (Mekala et al., 2017)further extends this from sentences to docu-ments by employing soft and sparse cluster-ing over pre-computed word vectors. How-ever, both techniques ignore the polysemyand contextual character of words. In thispaper, we address this issue by proposingSCDV+BERT(ctxd), a simple and effective un-supervised representation that combines con-textualized BERT (Devlin et al., 2019) basedword embedding for word sense disambigua-tion with SCDV soft clustering approach. Weshow that our embeddings outperform origi-nal SCDV, pre-train BERT, and several otherbaselines on many classification datasets. Wealso demonstrate our embeddings effective-ness on other tasks, such as concept match-ing and sentence similarity. In addition,we show that SCDV+BERT(ctxd) outperformsfine-tune BERT and different embedding ap-proaches in scenarios with limited data andonly few shots examples.</abstract>
    <identifier type="citekey">gupta-gupta-2021-unsupervised</identifier>
    <identifier type="doi">10.18653/v1/2021.sustainlp-1.17</identifier>
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        <url>https://aclanthology.org/2021.sustainlp-1.17</url>
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%0 Conference Proceedings
%T Unsupervised Contextualized Document Representation
%A Gupta, Ankur
%A Gupta, Vivek
%Y Moosavi, Nafise Sadat
%Y Gurevych, Iryna
%Y Fan, Angela
%Y Wolf, Thomas
%Y Hou, Yufang
%Y Marasović, Ana
%Y Ravi, Sujith
%S Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Virtual
%F gupta-gupta-2021-unsupervised
%X Several NLP tasks need the effective repre-sentation of text documents.Arora et al.,2017 demonstrate that simple weighted aver-aging of word vectors frequently outperformsneural models. SCDV (Mekala et al., 2017)further extends this from sentences to docu-ments by employing soft and sparse cluster-ing over pre-computed word vectors. How-ever, both techniques ignore the polysemyand contextual character of words. In thispaper, we address this issue by proposingSCDV+BERT(ctxd), a simple and effective un-supervised representation that combines con-textualized BERT (Devlin et al., 2019) basedword embedding for word sense disambigua-tion with SCDV soft clustering approach. Weshow that our embeddings outperform origi-nal SCDV, pre-train BERT, and several otherbaselines on many classification datasets. Wealso demonstrate our embeddings effective-ness on other tasks, such as concept match-ing and sentence similarity. In addition,we show that SCDV+BERT(ctxd) outperformsfine-tune BERT and different embedding ap-proaches in scenarios with limited data andonly few shots examples.
%R 10.18653/v1/2021.sustainlp-1.17
%U https://aclanthology.org/2021.sustainlp-1.17
%U https://doi.org/10.18653/v1/2021.sustainlp-1.17
%P 166-173

Markdown (Informal)

[Unsupervised Contextualized Document Representation](https://aclanthology.org/2021.sustainlp-1.17) (Gupta & Gupta, sustainlp 2021)

• Unsupervised Contextualized Document Representation (Gupta & Gupta, sustainlp 2021)

ACL

• Ankur Gupta and Vivek Gupta. 2021. Unsupervised Contextualized Document Representation. In Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, pages 166–173, Virtual. Association for Computational Linguistics.