In particular, we will assume we have access to a database m) and their corresponding true label-sequences y (m) .
we will predict a labelingŷ for x by considering each token x t , selecting a labeled token x  we consider a very simple token-level model for this label-agnostic copying, where the probability that x's t'th label y t is equal to y , and is independent of the surrounding labels, conditioned on x and D. 2 we define where the above probability is normalized over all label tokens of all label-sequences in D. Above, (both in R D ) represent the contextual word embeddings of the t'th token in x and the k'th token in x (m) , respectively, as obtained by running a deep sequence-model over x and over x (m) .