UGENT-LT3 SCATE system for machine translation quality estimation
- Author
- Arda Tezcan (UGent) , Veronique Hoste (UGent) , Bart Desmet (UGent) and Lieve Macken (UGent)
- Organization
- Abstract
- This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima-tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate quality. The sen-tence-level QE system re-uses the word level predictions of the word-level QE system. We experimented with different learning methods and observe improve-ments over the baseline system for word-level QE with the use of the new features and by combining learning methods into ensembles. For sentence-level QE we show that using a single feature based on word-level predictions can perform better than the baseline system and using this in combination with additional features led to further improvements in performance.
- Keywords
- machine learning, machine translation, quality estimation
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6934900
- MLA
- Tezcan, Arda, et al. “UGENT-LT3 SCATE System for Machine Translation Quality Estimation.” Tenth Workshop on Statistical Machine Translation, Proceedings, 2015.
- APA
- Tezcan, A., Hoste, V., Desmet, B., & Macken, L. (2015). UGENT-LT3 SCATE system for machine translation quality estimation. Tenth Workshop on Statistical Machine Translation, Proceedings. Presented at the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal.
- Chicago author-date
- Tezcan, Arda, Veronique Hoste, Bart Desmet, and Lieve Macken. 2015. “UGENT-LT3 SCATE System for Machine Translation Quality Estimation.” In Tenth Workshop on Statistical Machine Translation, Proceedings.
- Chicago author-date (all authors)
- Tezcan, Arda, Veronique Hoste, Bart Desmet, and Lieve Macken. 2015. “UGENT-LT3 SCATE System for Machine Translation Quality Estimation.” In Tenth Workshop on Statistical Machine Translation, Proceedings.
- Vancouver
- 1.Tezcan A, Hoste V, Desmet B, Macken L. UGENT-LT3 SCATE system for machine translation quality estimation. In: Tenth Workshop on Statistical Machine Translation, Proceedings. 2015.
- IEEE
- [1]A. Tezcan, V. Hoste, B. Desmet, and L. Macken, “UGENT-LT3 SCATE system for machine translation quality estimation,” in Tenth Workshop on Statistical Machine Translation, Proceedings, Lisbon, Portugal, 2015.
@inproceedings{6934900,
abstract = {{This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima-tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate quality. The sen-tence-level QE system re-uses the word level predictions of the word-level QE system. We experimented with different learning methods and observe improve-ments over the baseline system for word-level QE with the use of the new features and by combining learning methods into ensembles. For sentence-level QE we show that using a single feature based on word-level predictions can perform better than the baseline system and using this in combination with additional features led to further improvements in performance.}},
author = {{Tezcan, Arda and Hoste, Veronique and Desmet, Bart and Macken, Lieve}},
booktitle = {{Tenth Workshop on Statistical Machine Translation, Proceedings}},
keywords = {{machine learning,machine translation,quality estimation}},
language = {{eng}},
location = {{Lisbon, Portugal}},
title = {{UGENT-LT3 SCATE system for machine translation quality estimation}},
year = {{2015}},
}