A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation

Patrick Simianer, Sariya Karimova, Stefan Riezler


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.
Anthology ID:
C16-2004
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
16–20
Language:
URL:
https://aclanthology.org/C16-2004
DOI:
Bibkey:
Cite (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.
Cite (Informal):
A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation (Simianer et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-2004.pdf