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        <title>A Deep Learning Curve for Post-Editing 2</title>
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            <title>Proceedings of Machine Translation Summit XVII: Tutorial Abstracts</title>
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    <abstract>In the last couple of years, machine translation technology has seen major changes with the breakthrough of neural machine translation (NMT), a growing number of providers and translation platforms. Machine Translation generally is experiencing a peak in demand from translation buyers, thanks to Machine Learning and AI being omnipresent in the media and at industry events. At the same time, new models for defining translation quality are becoming more widely adopted. These changes have profound implications for translators, LSPs and translation buyers: translators have to adjust their post-editing approaches, while LSPs and translation buyers are faced with decisions on selecting providers, best approaches for updating MT systems, financial investments, integrating tools, and getting the timing for implementation right for an optimum ROI.In this tutorial on MT and post-editing we would like to continue sharing the latest trends in the field of MT technologies, and discuss their impact on post-editing practices as well as integrating MT on large, multi-language translation programs. We will look at tool compatibility, different use cases of MT and dynamic quality models, and share our experience of measuring performance.</abstract>
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%0 Conference Proceedings
%T A Deep Learning Curve for Post-Editing 2
%A Marg, Lena
%A Yanishevsky, Alex\textgreater
%Y Rossi, Laura
%S Proceedings of Machine Translation Summit XVII: Tutorial Abstracts
%D 2019
%8 August
%I European Association for Machine Translation
%C Dublin, Ireland
%F marg-yanishevsky-2019-deep
%X In the last couple of years, machine translation technology has seen major changes with the breakthrough of neural machine translation (NMT), a growing number of providers and translation platforms. Machine Translation generally is experiencing a peak in demand from translation buyers, thanks to Machine Learning and AI being omnipresent in the media and at industry events. At the same time, new models for defining translation quality are becoming more widely adopted. These changes have profound implications for translators, LSPs and translation buyers: translators have to adjust their post-editing approaches, while LSPs and translation buyers are faced with decisions on selecting providers, best approaches for updating MT systems, financial investments, integrating tools, and getting the timing for implementation right for an optimum ROI.In this tutorial on MT and post-editing we would like to continue sharing the latest trends in the field of MT technologies, and discuss their impact on post-editing practices as well as integrating MT on large, multi-language translation programs. We will look at tool compatibility, different use cases of MT and dynamic quality models, and share our experience of measuring performance.
%U https://aclanthology.org/W19-7603

Markdown (Informal)

[A Deep Learning Curve for Post-Editing 2](https://aclanthology.org/W19-7603) (Marg & Yanishevsky, MTSummit 2019)

• A Deep Learning Curve for Post-Editing 2 (Marg & Yanishevsky, MTSummit 2019)

ACL

• Lena Marg and Alex> Yanishevsky. 2019. A Deep Learning Curve for Post-Editing 2. In Proceedings of Machine Translation Summit XVII: Tutorial Abstracts, Dublin, Ireland. European Association for Machine Translation.