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    <titleInfo>
        <title>Paralinguist Assessment Decision Factors For Machine Translation Output: A Case Study</title>
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    <name type="personal">
        <namePart type="given">Carol</namePart>
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        <namePart type="given">Jocelyn</namePart>
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    <name type="personal">
        <namePart type="given">Florence</namePart>
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    <name type="personal">
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        <dateIssued>2010-oct 31-nov 4</dateIssued>
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            <title>Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Government MT User Program</title>
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            <publisher>Association for Machine Translation in the Americas</publisher>
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    <abstract>We describe a case study that presents a framework for examining whether Machine Translation (MT) output enables translation professionals to translate faster while at the same time producing better quality translations than without MT output. We seek to find decision factors that enable a translation professional, known as a Paralinguist, to determine whether MT output is of sufficient quality to serve as a “seed translation” for post-editors. The decision factors, unlike MT developers’ automatic metrics, must function without a reference translation. We also examine the correlation of MT developers’ automatic metrics with error annotators’ assessments of post-edited translations.</abstract>
    <identifier type="citekey">van-ess-dykema-etal-2010-paralinguist</identifier>
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        <url>https://aclanthology.org/2010.amta-government.1</url>
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%0 Conference Proceedings
%T Paralinguist Assessment Decision Factors For Machine Translation Output: A Case Study
%A Van Ess-Dykema, Carol
%A Phillips, Jocelyn
%A Reeder, Florence
%A Gerber, Laurie
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Government MT User Program
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F van-ess-dykema-etal-2010-paralinguist
%X We describe a case study that presents a framework for examining whether Machine Translation (MT) output enables translation professionals to translate faster while at the same time producing better quality translations than without MT output. We seek to find decision factors that enable a translation professional, known as a Paralinguist, to determine whether MT output is of sufficient quality to serve as a “seed translation” for post-editors. The decision factors, unlike MT developers’ automatic metrics, must function without a reference translation. We also examine the correlation of MT developers’ automatic metrics with error annotators’ assessments of post-edited translations.
%U https://aclanthology.org/2010.amta-government.1

Markdown (Informal)

[Paralinguist Assessment Decision Factors For Machine Translation Output: A Case Study](https://aclanthology.org/2010.amta-government.1) (Van Ess-Dykema et al., AMTA 2010)

• Paralinguist Assessment Decision Factors For Machine Translation Output: A Case Study (Van Ess-Dykema et al., AMTA 2010)

ACL

• Carol Van Ess-Dykema, Jocelyn Phillips, Florence Reeder, and Laurie Gerber. 2010. Paralinguist Assessment Decision Factors For Machine Translation Output: A Case Study. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Government MT User Program, Denver, Colorado, USA. Association for Machine Translation in the Americas.