Deep learning-based techniques to enhance the precision of phrase-based statistical machine translation system for Indian languages

The paper focuses on improving the existing phrase-based statistical machine translation (PB-SMT) system by integrating deep learning knowledge to it. In this paper, a deep learning-based PB-SMT system for Indian languages is developed, so as to improve the conditional probability of the phrase-table and replaced the neural probabilistic language model with the existing back off algorithm of n-gram language model to improve the performance of language model. It is shown that the deep feature-based PB-SMT is better than the standard PB-SMT system. It is shown the significance of integrating manually created dictionaries that has been trained as separate translational model can enhance the result of statistical machine translation system when decoding. For automatic evaluation, it is shown that RIBES being a better evaluation metric for Indian languages compared to BLEU, a standard one.