Error Detection for Statistical Machine Translation Based on Feature Comparison and Maximum Entropy Model Classifier
Sha Wang · Beijing Daxue Xuebao. Zirankexueban · 2013
The authors firstly introduce three typical word posterior probabilities(WPP) for error detection and classification,which are fixed position WPP,sliding window WPP,and alignment-based WPP,and analyzes their impact on the detection performance.Then each WPP feature is combined with three linguistic features(Word,POS and LG Parsing knowledge) over the maximum entropy classifier to predict the translation errors.Experimental results on Chinese-to-English NIST datasets show that the influences of different WPP features on the classification error rate(CER) are significant,and the combination of WPP with linguistic features can significantly reduce the CER and improve the prediction capability of the classifier.