If we were to naïvely add c i = 1 to all samples of the training data, the model would learn that c i is constant rather than random, and therefore that it is independent of its parents, s i−1 and s i .
this naïvely-trained model would assume that P (c i = 1|s i−1 , s i ) = 1 ∀ (s i−1 , s i ), and when used to tag the sentences in the test-set (also labeled with c i = 1), it would maximize this simplified joint probability in which the relationship between s i−1 and s i has been lost: In order to induce and thereby have the model learn the relationship between the adjacent tags s i−1 and s i , the training has to be modified to include samples that are labeled with c i = 0.