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        <title>Unsupervised Neural Text Simplification</title>
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        <namePart type="given">Sai</namePart>
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    <name type="personal">
        <namePart type="given">Abhijit</namePart>
        <namePart type="family">Mishra</namePart>
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    <name type="personal">
        <namePart type="given">Anirban</namePart>
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    <name type="personal">
        <namePart type="given">Karthik</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders, crucially assisted by discrimination-based losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. It also outperforms viable unsupervised baselines. Adding a few labeled pairs helps improve the performance further.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1198</identifier>
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%0 Conference Proceedings
%T Unsupervised Neural Text Simplification
%A Surya, Sai
%A Mishra, Abhijit
%A Laha, Anirban
%A Jain, Parag
%A Sankaranarayanan, Karthik
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F surya-etal-2019-unsupervised
%X The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders, crucially assisted by discrimination-based losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. It also outperforms viable unsupervised baselines. Adding a few labeled pairs helps improve the performance further.
%R 10.18653/v1/P19-1198
%U https://aclanthology.org/P19-1198
%U https://doi.org/10.18653/v1/P19-1198
%P 2058-2068

Markdown (Informal)

[Unsupervised Neural Text Simplification](https://aclanthology.org/P19-1198) (Surya et al., ACL 2019)

• Unsupervised Neural Text Simplification (Surya et al., ACL 2019)

ACL

• Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, and Karthik Sankaranarayanan. 2019. Unsupervised Neural Text Simplification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2058–2068, Florence, Italy. Association for Computational Linguistics.