Urdu to Punjabi Machine Translation: An Incremental Training Approach

Abstract

The statistical machine translation approach is highly popular in automatic translation research area and promising approach to yield good accuracy. Efforts have been made to develop Urdu to Punjabi statistical machine translation system. The system is based on an incremental training approach to train the statistical model. In place of the parallel sentences corpus has manually mapped phrases which were used to train the model. In preprocessing phase, various rules were used for tokenization and segmentation processes. Along with these rules, text classification system was implemented to classify input text to predefined classes and decoder translates given text according to selected domain by the text classifier. The system used Hidden Markov Model(HMM) for the learning process and Viterbi algorithm has been used for decoding. Experiment and evaluation have shown that simple statistical model like HMM yields good accuracy for a closely related language pair like Urdu-Punjabi. The system has achieved 0.86 BLEU score and in manual testing and got more than 85% accuracy.