Finally, we would like the model to be less dependent on the gold actions generated by the aligner, which is uninformed of the downstream task, and that at training, the model can choose alternative action sequences leading to correct predictions, if that helps it generalize.
to address all these issues at once, we train the model by minimizing the expected risk (Och, 2003;Smith and Eisner, 2006) of the actual training data t = {(x (l) , y (l) )} N l=1 : where y is computed from a and x, and the risk is given by a combination of normalized Levenshtein distance (NLD) and accuracy: an action sequence a attains the lowest risk of −1 if its corresponding output sequence y is identical to y (l) of the training sample and the highest risk of +1 if the number of edits from y to y (l) equals the maximum of their lengths.