  Being less resource languages, Indian-Indian and English-Indian language MT
system developments faces the difficulty to translate various lexical
phenomena. In this paper, we present our work on a comparative study of 440
phrase-based statistical trained models for 110 language pairs across 11 Indian
languages. We have developed 110 baseline Statistical Machine Translation
systems. Then we have augmented the training corpus with Indowordnet synset
word entries of lexical database and further trained 110 models on top of the
baseline system. We have done a detailed performance comparison using various
evaluation metrics such as BLEU score, METEOR and TER. We observed significant
improvement in evaluations of translation quality across all the 440 models
after using the Indowordnet. These experiments give a detailed insight in two
ways : (1) usage of lexical database with synset mapping for resource poor
languages (2) efficient usage of Indowordnet sysnset mapping. More over, synset
mapped lexical entries helped the SMT system to handle the ambiguity to a great
extent during the translation.
