  This paper describes combinations of word vector representation and character
vector representation in English-Indonesian neural machine translation (NMT).
Six configurations of NMT models were built with different input vector
representations: word-based, combination of word and character representation
using bidirectional LSTM(bi-LSTM), combination of word and character
representation using CNN, combination of word and character representation by
combining bi-LSTM and CNN by three different vector operations: addition,
pointwise multiplication, and averaging. The experiment results showed that NMT
models with concatenation of word and character representation obtained BLEU
score higher than baseline model, ranging from 9.14 points to 11.65 points, for
all models that combining both word and character representation, except the
model that combining word and character representation using both bi-LSTM and
CNN by addition operation. The highest BLEU score achieved was 42.48 compared
to the 30.83 of the baseline model.
