In practice, neural network models for machine translation are usually trained by maximizing the likelihood of the training data, either via a crossentropy objective (Mikolov et al., 2010;Schwenk et al., 2012) or more recently, noise-contrastive estimation (Vaswani et al., 2013).
most features are local, meaning that all required information for them to assign a score is available within the state.