Word Confidence Estimation and its Integration in Sentence Quality Estimation for Machine Translation
Résumé
This paper proposes some ideas to build an effective estima-tor, which predicts the quality of words in a Machine Translation (MT) output. We integrate a number of features of various types (system-based, lexical, syntactic and semantic) into the conventional feature set, for our baseline classifier training. Once having experiments with all features , we deploy a " Feature Selection " strategy to filter the best performing ones. Then, a method that combines multiple " weak " classifiers to build a strong " composite " classifier by taking advantage of their com-plementarity allows us achieve a better performance in term of F score. Finally, we exploit word confidence scores for improving the estimation system at sentence level.
Fichier principal
WordConfidenceEstimationAndItsIntegrationInSentenceQualityEstimationForMachineTranslation.pdf (340.83 Ko)
Télécharger le fichier
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Loading...