The final step in the argument is to make the case that the algorithms described in the current paper may effectively be optimizing a criterion of this kind.
the auxiliary function β(θ A ) is defined as the log-likelihood of noisy (c, q, a) triples generated from unlabeled data using the C → A and C, A → Q models; constraining the parameters θ A to achieve a relatively high value on β(θ A ) is achieved by pre-training the model on these examples.