Learning to Translate in Real-time with Neural Machine Translation

Understand

Translating in real-time, a.k.a.

• simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods.
• We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment.
• To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting.