Phrase table filtration based on virtual context in phrase-based statistical machine translation

In statistical machine translation system, automatically extracted phrase table inevitably contains a large number of errors and redundant phrase pairs, which causes excessive waste of time and space in decoding and affects translation quality. In order to solve this problem, we propose a method for filtering phrase table based in which virtual context is introduced to calculate an incremental quantity in language model for score of phrase pair. By considering the maximum and minimum incremental quantity in score from the virtual context, we design a filtering strategy by re-ranking phrase pairs. We conducted experiments on NTCIR-9 data to verify the method. The experimental results show that when the size of phrase table was reduced to 47% of the original, the translation quality was improved slightly; when the size was reduced to 30% of the original, only slight decline occurred in translation quality. The experimental results indicate that this method can effectively filter out the redundant phrase pairs of the phrase table.