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        <title>Sentence Compression for Arbitrary Languages via Multilingual Pivoting</title>
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    <name type="personal">
        <namePart type="given">Jonathan</namePart>
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    <name type="personal">
        <namePart type="given">Rico</namePart>
        <namePart type="family">Sennrich</namePart>
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    <name type="personal">
        <namePart type="given">Mirella</namePart>
        <namePart type="family">Lapata</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.</abstract>
    <identifier type="citekey">mallinson-etal-2018-sentence</identifier>
    <identifier type="doi">10.18653/v1/D18-1267</identifier>
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        <date>2018-oct-nov</date>
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            <start>2453</start>
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%0 Conference Proceedings
%T Sentence Compression for Arbitrary Languages via Multilingual Pivoting
%A Mallinson, Jonathan
%A Sennrich, Rico
%A Lapata, Mirella
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mallinson-etal-2018-sentence
%X In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.
%R 10.18653/v1/D18-1267
%U https://aclanthology.org/D18-1267
%U https://doi.org/10.18653/v1/D18-1267
%P 2453-2464

Markdown (Informal)

[Sentence Compression for Arbitrary Languages via Multilingual Pivoting](https://aclanthology.org/D18-1267) (Mallinson et al., EMNLP 2018)

• Sentence Compression for Arbitrary Languages via Multilingual Pivoting (Mallinson et al., EMNLP 2018)

ACL

• Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2018. Sentence Compression for Arbitrary Languages via Multilingual Pivoting. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2453–2464, Brussels, Belgium. Association for Computational Linguistics.