Quality estimation for machine translation using the joint method of evaluation criteria and statistical modeling

This paper is to introduce our participation in the WMT13 shared tasks on Quality Estimation for machine translation without using reference translations. We submitted the results for Task 1.1 (sentence-level quality estimation), Task 1.2 (system selection) and Task 2 (word-level quality estimation). In Task 1.1, we used an enhanced version of BLEU metric without using reference translations to evaluate the translation quality. In Task 1.2, we utilized a probability model Naïve Bayes (NB) as a classification algorithm with the features borrowed from the traditional evaluation metrics. In Task 2, to take the contextual information into account, we employed a discriminative undirected probabilistic graphical model Conditional random field (CRF), in addition to the NB algorithm. The training experiments on the past WMT corpora showed that the designed methods of this paper yielded promising results especially the statistical models of CRF and NB. The official results show that our CRF model achieved the highest F-score 0.8297 in binary classification of Task 2.