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        <title>Zero-Shot Crosslingual Sentence Simplification</title>
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        <namePart type="given">Jonathan</namePart>
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    <name type="personal">
        <namePart type="given">Mirella</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Crosslingual Sentence Simplification
%A Mallinson, Jonathan
%A Sennrich, Rico
%A Lapata, Mirella
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F mallinson-etal-2020-zero
%X Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.
%R 10.18653/v1/2020.emnlp-main.415
%U https://aclanthology.org/2020.emnlp-main.415
%U https://doi.org/10.18653/v1/2020.emnlp-main.415
%P 5109-5126

Markdown (Informal)

[Zero-Shot Crosslingual Sentence Simplification](https://aclanthology.org/2020.emnlp-main.415) (Mallinson et al., EMNLP 2020)

• Zero-Shot Crosslingual Sentence Simplification (Mallinson et al., EMNLP 2020)

ACL

• Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2020. Zero-Shot Crosslingual Sentence Simplification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5109–5126, Online. Association for Computational Linguistics.