  This paper discusses Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to the NLP Tools Contest on Statistical Machine Translation
in Indian Languages (ILSMT) 2014 (collocated with ICON 2014). The objective of
the contest was to explore the effectiveness of Statistical Machine Translation
(SMT) for Indian language to Indian language and English-Hindi machine
translation. In this paper, we have proposed that suffix separation and word
splitting for SMT from agglutinative languages to Hindi significantly improves
over the baseline (BL). We have also shown that the factored model with
reordering outperforms the phrase-based SMT for English-Hindi (\enhi). We
report our work on all five pairs of languages, namely Bengali-Hindi (\bnhi),
Marathi-Hindi (\mrhi), Tamil-Hindi (\tahi), Telugu-Hindi (\tehi), and \enhi for
Health, Tourism, and General domains.
