Extraction of reordering rules for statistical machine translation

Word reordering is an important problem for translation between languages which have different structures such as Subject-Verb-Object and Subject-Object-Verb. This paper presents a statistical method for extraction of linguistic rules using chunk to reorder the output of the baseline statistical machine translation system for improved performance. The experiments are based on the TDIL sample tourism corpus of English-Hindi language pair which consists of 1000 sentence pairs out of which 900 sentence pairs are used for training, 50 sentences for tuning and 50 sentences for testing. Finally, the output of the machine translation system, augmented by these rules, is evaluated by using BLEU and NIST metrics. The BLEU score improves by more than 2% in comparison to the baseline SMT system. The results are compared with those of Google translation system which has been trained on a huge corpus. We got a 0.1 point improvement in terms of NIST score, in comparison to Google Translation. Thus, we have comparable results with such a small corpus of 900 sentence pairs for training. This paper is an effort to improve the performance of SMT with a small corpus by using linguistic rules where the rules are automatically generated instead of made by linguist.