  In this paper, an extended combined approach of phrase based statistical
machine translation (SMT), example based MT (EBMT) and rule based MT (RBMT) is
proposed to develop a novel hybrid data driven MT system capable of
outperforming the baseline SMT, EBMT and RBMT systems from which it is derived.
In short, the proposed hybrid MT process is guided by the rule based MT after
getting a set of partial candidate translations provided by EBMT and SMT
subsystems. Previous works have shown that EBMT systems are capable of
outperforming the phrase-based SMT systems and RBMT approach has the strength
of generating structurally and morphologically more accurate results. This
hybrid approach increases the fluency, accuracy and grammatical precision which
improve the quality of a machine translation system. A comparison of the
proposed hybrid machine translation (HTM) model with renowned translators i.e.
Google, BING and Babylonian is also presented which shows that the proposed
model works better on sentences with ambiguity as well as comprised of idioms
than others.
