We use an architecture motivated by latent predictor networks (Ling et al., 2016) to force the model to learn when to copy vs. directly predict the word, without direct supervision of what action to choose.
at every time step i, two latent predictors generate the probability of generating word w i , a pointer network C p (Vinyals et al., 2015) which can copy a word from the context paragraph, and a vocabulary predictor V p which directly generates a probability distribution of choosing a word w i from a predefined vocabulary.