Confidence Estimation for Machine Translation using New Syntactic and Lexico-semantic Features

Machine translation has been developed over last years. But this technology is still not able to exactly translate texts. Also post-editing the output may takes longer time than the translation process. So having a quality estimation of machine translation output can be very useful. Moreover, Confidence Estimation can be useful for some applications that their goal is to improve machine translation quality such as system combination, regenerating and pruning. But there is not yet any completely satisfactory method for CE task. We propose 5 syntactic and lexico-semantic features that are never used for confidence estimation task. The experimental results show that proposed lexico-semantic feature outperforms the best baseline system (2) by 9.63% in CER, 8.5% in F-measure and 5.1% in negative class F-measure. Moreover the combination of proposed syntactic features outperforms the best baseline system by 4.49% in CER, 4.1% in F-measure and 2% in negative class F-measure.