For each local prediction, we use the history h representing the context of making a single transliteration prediction.
we predict each letter e i based on the pair Formally, we map H×E into a d-dimensional feature space ϕ: H×E → R d , where each ϕ k (h,e)(k∈{1,..,d}) corresponds to a condition defined in terms of the history h and the currently predicted letter e. In order to model string termination, we augment E with a sentinel symbol $, and we append $ to each e from D. Given a transliteration dictionary D, we transform the dictionary in a set of |E| binary learning problems.