  We investigate the potential of attention-based neural machine translation in
simultaneous translation. We introduce a novel decoding algorithm, called
simultaneous greedy decoding, that allows an existing neural machine
translation model to begin translating before a full source sentence is
received. This approach is unique from previous works on simultaneous
translation in that segmentation and translation are done jointly to maximize
the translation quality and that translating each segment is strongly
conditioned on all the previous segments. This paper presents a first step
toward building a full simultaneous translation system based on neural machine
translation.
