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Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct hidden Markov model (HMM) with neural network-based lexicon and alignment models, which are trained jointly usingdoi:10.18653/v1/p17-2020 dblp:conf/acl/WangAZN17 fatcat:wjcewdqrkfcqjpz53ow3hcq7qe