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        <title>A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation</title>
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        <namePart type="given">Patrick</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations</title>
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            <namePart type="given">Hideo</namePart>
            <namePart type="family">Watanabe</namePart>
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    <abstract>Adaptive machine translation (MT) systems are a promising approach for improving the effectiveness of computer-aided translation (CAT) environments. There is, however, virtually only theoretical work that examines how such a system could be implemented. We present an open source post-editing interface for adaptive statistical MT, which has in-depth monitoring capabilities and excellent expandability, and can facilitate practical studies. To this end, we designed text-based and graphical post-editing interfaces. The graphical interface offers means for displaying and editing a rich view of the MT output. Our translation systems may learn from post-edits using several weight, language model and novel translation model adaptation techniques, in part by exploiting the output of the graphical interface. In a user study we show that using the proposed interface and adaptation methods, reductions in technical effort and time can be achieved.</abstract>
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%0 Conference Proceedings
%T A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation
%A Simianer, Patrick
%A Karimova, Sariya
%A Riezler, Stefan
%Y Watanabe, Hideo
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F simianer-etal-2016-post
%X Adaptive machine translation (MT) systems are a promising approach for improving the effectiveness of computer-aided translation (CAT) environments. There is, however, virtually only theoretical work that examines how such a system could be implemented. We present an open source post-editing interface for adaptive statistical MT, which has in-depth monitoring capabilities and excellent expandability, and can facilitate practical studies. To this end, we designed text-based and graphical post-editing interfaces. The graphical interface offers means for displaying and editing a rich view of the MT output. Our translation systems may learn from post-edits using several weight, language model and novel translation model adaptation techniques, in part by exploiting the output of the graphical interface. In a user study we show that using the proposed interface and adaptation methods, reductions in technical effort and time can be achieved.
%U https://aclanthology.org/C16-2004
%P 16-20

Markdown (Informal)

[A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation](https://aclanthology.org/C16-2004) (Simianer et al., COLING 2016)

• A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation (Simianer et al., COLING 2016)

ACL

• Patrick Simianer, Sariya Karimova, and Stefan Riezler. 2016. A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 16–20, Osaka, Japan. The COLING 2016 Organizing Committee.