Hokamp, Chris, Calixto, Iacer ORCID: 0000-0001-6244-7906, Wagner, Joachim ORCID: 0000-0002-8290-3849 and Zhang, Jian (2014) Target-centric features for translation quality estimation. In: Workshop on Statistical Machine Translation, 26-27 June 2014, Baltimore, Maryland, USA. ISBN 9781713862482
Abstract
We describe the DCU-MIXED and DCU-SVR submissions to the WMT-14 Quality Estimation task 1.1, predicting sentence-level perceived post-editing effort. Feature design focuses on target-side features as we hypothesise that the source side has little effect on the quality of human translations, which are included in task 1.1 of this year’s WMT Quality Estimation shared task. We experiment with features of the QuEst framework, features of our past work, and three novel feature sets. Despite these efforts, our two systems perform poorly in the competition. Follow up experiments indicate that the poor performance is due to improperly optimised parameters.
Metadata
Item Type: | Conference or Workshop Item (Poster) |
---|---|
Event Type: | Workshop |
Refereed: | Yes |
Subjects: | Computer Science > Artificial intelligence Computer Science > Computational linguistics Computer Science > Machine translating |
DCU Faculties and Centres: | Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL) Research Initiatives and Centres > National Centre for Language Technology (NCLT) DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
Published in: | Proceedings of the Ninth Workshop on Statistical Machine Translation. . Association for Computational Linguistics (ACL). ISBN 9781713862482 |
Publisher: | Association for Computational Linguistics (ACL) |
Official URL: | https://doi.org/10.3115/v1/W14-3341 |
Copyright Information: | © 2014 The Association for Computational Linguistics. |
Funders: | European Commission under the 7th Framework Programme, specifically its Marie Curie Programme 317471, Science Foundation Ireland (Grant 12/CE/I2267) |
ID Code: | 20712 |
Deposited On: | 27 Apr 2023 11:34 by Joachim Wagner . Last Modified 27 Apr 2023 11:34 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
108kB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record