Mitigation of Data Sparsity in Classifier-Based Translation

The concept classifier has been used as a translation unit in speech-to-speech translation systems. However, the sparsity of the training data is the bottle neck of its effectiveness. Here, a new method based on using a statistical machine translation system has been introduced to mitigate the effects of data sparsity for training classifiers. Also, the effects of the background model which is necessary to compensate the above problem, is investigated. Experimental evaluation in the context of crosslingual doctor-patient interaction application show the superiority of the proposed method.