  In this study, we present an analysis regarding the performance of the
state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple
Indian languages. We report baseline systems on several language pairs. The
motivation of this study is to promote the development of SMT and linguistic
resources for these language pairs, as the current state-of-the-art is quite
bleak due to sparse data resources. The success of an SMT system is contingent
on the availability of a large parallel corpus. Such data is necessary to
reliably estimate translation probabilities. We report the performance of
baseline systems translating from Indian languages (Bengali, Guajarati, Hindi,
Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10%
accurate results for all the language pairs.
