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 EnglishSpanish 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.
REF investigates the impact of a large set of linguistically inspired features on quality estimation accuracy, which are not able to outperform the shallower features based on word statistics.
Linguistic Features for Quality Estimation
Proceedings of the Seventh Workshop on Statistical Machine Translation
Computer Science