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
- Editor:
- Hideo Watanabe
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 16–20
- Language:
- URL:
- https://aclanthology.org/C16-2004
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/landing_page/C16-2004.pdf