Error Detection for Statistical Machine Translation Using Linguistic Features

This work uses a maximum entropy classifier to predict translation errors by integrating word posterior probability feature and linguistic features and shows that linguistic features alone outperform word posterior probability based confidence estimation in error detection.

Abstract

Automatic error detection is desired in the post-processing to improve machine translation quality. The previous work is largely based on confidence estimation using system-based features, such as word posterior probabilities calculated from N-best lists or word lattices. We propose to incorporate two groups of linguistic features, which convey information from outside machine translation systems, into error detection: lexical and syntactic features. We use a maximum entropy classifier to predict translation errors by integrating word posterior probability feature and linguistic features. The experimental results show that 1) linguistic features alone outperform word posterior probability based confidence estimation in error detection; and 2) linguistic features can further provide complementary information when combined with word confidence scores, which collectively reduce the classification error rate by 18.52 % and improve the F measure by 16.37%. 1