  This paper presents the system description of Machine Translation (MT)
system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT
Shared Task. In our experiments, we (the RGNLP team) explore both statistical
and neural methods across all language pairs. (We further present an extensive
comparison of language-related problems for both the approaches in the context
of low-resourced settings.) Our PBSMT models were highest score on all
automatic evaluation metrics in the English into Telugu, Hindi, Bengali, Tamil
portion of the shared task.
