Word Confidence Estimation and Its Integration in Sentence Quality Estimation for Machine Translation

This paper proposes some ideas to build an effective estimator, 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. After the 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 complementarity allows us to achieve a better performance in term of F score. Finally, we exploit word confidence scores for improving the estimation system at sentence level.