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        <title>Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation</title>
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        <namePart type="given">Samuel</namePart>
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        <namePart type="given">Rico</namePart>
        <namePart type="family">Sennrich</namePart>
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    <name type="personal">
        <namePart type="given">Martin</namePart>
        <namePart type="family">Volk</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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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.</abstract>
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        <date>2018-oct-nov</date>
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            <start>4791</start>
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%0 Conference Proceedings
%T Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation
%A Läubli, Samuel
%A Sennrich, Rico
%A Volk, Martin
%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 laubli-etal-2018-machine
%X Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.
%R 10.18653/v1/D18-1512
%U https://aclanthology.org/D18-1512
%U https://doi.org/10.18653/v1/D18-1512
%P 4791-4796

Markdown (Informal)

[Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://aclanthology.org/D18-1512) (Läubli et al., EMNLP 2018)

• Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation (Läubli et al., EMNLP 2018)

ACL

• Samuel Läubli, Rico Sennrich, and Martin Volk. 2018. Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4791–4796, Brussels, Belgium. Association for Computational Linguistics.