Can SMT and RBMT Improve each other’s Performance?- An Experiment with English-Hindi Translation

Rule-based machine translation (RBMT) and Statistical machine translation (SMT) are two well-known approaches for translation which have their own benefits. System architecture of SMT often complements RBMT, and the vice-versa. In this paper, we propose an effective method of serial coupling where we attempt to build a hybrid model that exploits the benefits of both the architectures. The first part of coupling is used to obtain good lexical selection and robustness, second part is used to improve syntax and the final one is designed to combine other modules along with the best phrase reordering. Our experiments on a English-Hindi product domain dataset show the effectiveness of the proposed approach with improvement in BLEU score.

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