@inproceedings{simianer-etal-2016-post,
title = "A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation",
author = "Simianer, Patrick and
Karimova, Sariya and
Riezler, Stefan",
editor = "Watanabe, Hideo",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-2004",
pages = "16--20",
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.",
}
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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)
ACL