<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mathur-etal-2019-putting">
    <titleInfo>
        <title>Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nitika</namePart>
        <namePart type="family">Mathur</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trevor</namePart>
        <namePart type="family">Cohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and system-level tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.</abstract>
    <identifier type="citekey">mathur-etal-2019-putting</identifier>
    <identifier type="doi">10.18653/v1/P19-1269</identifier>
    <location>
        <url>https://aclanthology.org/P19-1269</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>2799</start>
            <end>2808</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation
%A Mathur, Nitika
%A Baldwin, Timothy
%A Cohn, Trevor
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F mathur-etal-2019-putting
%X Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and system-level tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.
%R 10.18653/v1/P19-1269
%U https://aclanthology.org/P19-1269
%U https://doi.org/10.18653/v1/P19-1269
%P 2799-2808

Markdown (Informal)

[Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation](https://aclanthology.org/P19-1269) (Mathur et al., ACL 2019)

• Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation (Mathur et al., ACL 2019)

ACL

• Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2019. Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2799–2808, Florence, Italy. Association for Computational Linguistics.