Logographic Subword Model for Neural Machine Translation
(a) RNN Seq2Seq Model
['[BOLD] L', '[BOLD] PrePr', '[BOLD] TrgtV', '[BOLD] NN(Mb)', '[BOLD] T(hr)', '[BOLD] t(ms)', '[BOLD] BLEU']
We examine how our approach improves the performance of both the RNN seq2seq model Cho et al. ; Bahdanau et al. During encoding, every logographic word in those sentences is replaced by their corresponding abstract subword (symbol) vectors. New dictionaries are created from those encoded sentences. We then train both the RNN and CNN sequence-to-sequence models with them. Model sizes (NN), We observe that in the English-to-Chinese translation task, the model sizes are reduced by 37% and 77% respectively with RNN and CNN; the training time is 11% and 73% shorter; and the inference time is nearly halved in RNN and 36% shorter in CNN.