BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Introduction to Machine Learning
Introduction to Machine Learning
Error detection for statistical machine translation using linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Linguistic measures for automatic machine translation evaluation
Machine Translation
Goodness: a method for measuring machine translation confidence
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic projection of semantic structures: an application to pairwise translation ranking
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Findings of the 2012 workshop on statistical machine translation
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Statistical machine translation enhancements through linguistic levels: A survey
ACM Computing Surveys (CSUR)
Quality estimation for machine translation: some lessons learned
Machine Translation
Sentence-level ranking with quality estimation
Machine Translation
Investigating the contribution of linguistic information to quality estimation
Machine Translation
| Hi-index | 0.00 |
This paper describes a study on the contribution of linguistically-informed features to the task of quality estimation for machine translation at sentence level. A standard regression algorithm is used to build models using a combination of linguistic and non-linguistic features extracted from the input text and its machine translation. Experiments with English-Spanish translations show that linguistic features, although informative on their own, are not yet able to outperform shallower features based on statistics from the input text, its translation and additional corpora. However, further analysis suggests that linguistic information is actually useful but needs to be carefully combined with other features in order to produce better results.