  The Philippines is an archipelago composed of 7, 641 different islands with
more than 150 different languages. This linguistic differences and diversity,
though may be seen as a beautiful feature, have contributed to the difficulty
in the promotion of educational and cultural development of different domains
in the country. An effective machine translation system solely dedicated to
cater Philippine languages will surely help bridge this gap. In this research
work, a never before applied approach for language translation to a Philippine
language was used for a Cebuano to Tagalog translator. A Recurrent Neural
Network was used to implement the translator using OpenNMT sequence modeling
tool in TensorFlow. The performance of the translation was evaluated using the
BLEU Score metric. For the Cebuano to Tagalog translation, BLEU produced a
score of 20.01. A subword unit translation for verbs and copyable approach was
performed where commonly seen mistranslated words from the source to the target
were corrected. The BLEU score increased to 22.87. Though slightly higher, this
score still indicates that the translation is somehow understandable but is not
yet considered as a good translation.
