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

The RGNLP Machine Translation Systems for WAT 2018