Simple scripts for training and using EUREKA-MangoNMT

--train a Chinese to English NMT in spoken language. The validation perplexity with attention and feed-input should be around 22.84, 24.63 without feed-input
./train_ch2en.sh 


--given source language sentences, generate the output sequence using beam search. NOTE: odd lines in test data are useless for decoding, they can be blank lines
./generate_ch2en.sh lstm.s2s.z2e.decoder.best lstm.s2s.z2e.encoder.best lstm.s2s.z2e.encoder_reverse.best

--given source language sentences, generate the output sequence using beam search and similar training sentences to fine-tune the NMT model (One Sentence One Model for Neural Machine Translation)
./adapt_generate_ch2en.sh lstm.s2s.z2e.decoder.best lstm.s2s.z2e.encoder.best lstm.s2s.z2e.encoder_reverse.best


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More informaiton can refer the command options in codes!