Abstract:
This work explores the first evaluation of the
quality of neural machine translation between
Myanmar (Burmese) and Dawei (Tavoyan). We also
developed Myanmar-Dawei parallel corpus
(around 9K sentences) based on the Myanmar
language of ASEAN MT corpus. We implemented two
prominent neural machine translation systems:
Recurrent Neural Network (RNN) and Transformer
with syllable segmentation. We also investigated
various hyper-parameters such as batch size,
learning rate and cell types (GRU and LSTM). We
proved that LSTM cell type with RNN architecture is
the best for Dawei-Myanmar and Myanmar-Dawei
neural machine translation. Myanmar to Dawei NMT
achieved comparable results with PBSMT and
HPBSMT. Moreover, Dawei to Myanmar RNN
machine translation performance achieved higher
BLEU scores than PBSMT (+1.06 BLEU) and
HPBSMT (+1.37 BLEU) even with the limited
parallel corpus.
