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    <titleInfo>
        <title>Optimizing Statistical Machine Translation for Text Simplification</title>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
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        <namePart type="given">Courtney</namePart>
        <namePart type="family">Napoles</namePart>
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        <namePart type="given">Ellie</namePart>
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    <name type="personal">
        <namePart type="given">Quanze</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
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    <abstract>Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.</abstract>
    <identifier type="citekey">xu-etal-2016-optimizing</identifier>
    <identifier type="doi">10.1162/tacl_a_00107</identifier>
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        <url>https://aclanthology.org/Q16-1029</url>
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        <date>2016</date>
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            <start>401</start>
            <end>415</end>
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%0 Journal Article
%T Optimizing Statistical Machine Translation for Text Simplification
%A Xu, Wei
%A Napoles, Courtney
%A Pavlick, Ellie
%A Chen, Quanze
%A Callison-Burch, Chris
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F xu-etal-2016-optimizing
%X Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.
%R 10.1162/tacl_a_00107
%U https://aclanthology.org/Q16-1029
%U https://doi.org/10.1162/tacl_a_00107
%P 401-415

Markdown (Informal)

[Optimizing Statistical Machine Translation for Text Simplification](https://aclanthology.org/Q16-1029) (Xu et al., TACL 2016)

• Optimizing Statistical Machine Translation for Text Simplification (Xu et al., TACL 2016)

ACL

• Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016. Optimizing Statistical Machine Translation for Text Simplification. Transactions of the Association for Computational Linguistics, 4:401–415.