Abstract:
In this paper, a comparison between neural
based network that adopts Recurrent Neural Network
(RNN) language model and statistical based one with
N-gram language model is conducted for English-to-
Myanmar phrase-based statistical machine translation
(PBSMT). In this comparison, lexicalized reordering
models such as word-based, phrase-based and
hierarchical orientation models are used as an
additional reordering model to investigate the overall
performance of PBSMT. The perplexity value
evaluation of both language models showed that the
use of RNN obtains a more excellent result. According
to the obtained BLEU and RIBES scores and
additional human visual inspection, the English-to-
Myanmar PBSMT with RNN language model and
hierarchical reordering model is the best one in terms
of improving adequacy and fluency.
