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        <title>Document-level re-ranking with soft lexical and semantic features for statistical machine translation</title>
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    <name type="personal">
        <namePart type="given">Chenchen</namePart>
        <namePart type="family">Ding</namePart>
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    <name type="personal">
        <namePart type="given">Masao</namePart>
        <namePart type="family">Utiyama</namePart>
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    <name type="personal">
        <namePart type="given">Eiichiro</namePart>
        <namePart type="family">Sumita</namePart>
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        <dateIssued>2014-oct 22-26</dateIssued>
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            <title>Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Michel</namePart>
            <namePart type="family">Simard</namePart>
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    <abstract>We introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between a sentence and its context on the target side; the other feature is a crisp document-level token-type ratio of target-side translations for source-side words to model the lexical consistency in translation. The weights of introduced features are tuned to optimize the sentence- and document-level metrics simultaneously on the basis of Pareto optimality. Experimental results on two different schemes with different corpora illustrate that the proposed approach can efficiently and stably integrate document-level information into a sentence-level SMT system. The best improvements were approximately 0.5 BLEU on test sets with statistical significance.</abstract>
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        <date>2014-oct 22-26</date>
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            <start>110</start>
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%0 Conference Proceedings
%T Document-level re-ranking with soft lexical and semantic features for statistical machine translation
%A Ding, Chenchen
%A Utiyama, Masao
%A Sumita, Eiichiro
%Y Al-Onaizan, Yaser
%Y Simard, Michel
%S Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
%D 2014
%8 oct 22 26
%I Association for Machine Translation in the Americas
%C Vancouver, Canada
%F ding-etal-2014-document
%X We introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between a sentence and its context on the target side; the other feature is a crisp document-level token-type ratio of target-side translations for source-side words to model the lexical consistency in translation. The weights of introduced features are tuned to optimize the sentence- and document-level metrics simultaneously on the basis of Pareto optimality. Experimental results on two different schemes with different corpora illustrate that the proposed approach can efficiently and stably integrate document-level information into a sentence-level SMT system. The best improvements were approximately 0.5 BLEU on test sets with statistical significance.
%U https://aclanthology.org/2014.amta-researchers.9
%P 110-123

Markdown (Informal)

[Document-level re-ranking with soft lexical and semantic features for statistical machine translation](https://aclanthology.org/2014.amta-researchers.9) (Ding et al., AMTA 2014)

• Document-level re-ranking with soft lexical and semantic features for statistical machine translation (Ding et al., AMTA 2014)

ACL

• Chenchen Ding, Masao Utiyama, and Eiichiro Sumita. 2014. Document-level re-ranking with soft lexical and semantic features for statistical machine translation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 110–123, Vancouver, Canada. Association for Machine Translation in the Americas.