The connective-augmented recognizer is in analogy to an annotator with the help of connectives as in the human annotation process, and the implicit recognizer would be improved by learning from such an "informed" annotator.
we want the latent features extracted by the two models to match as closely as possible, which explicitly transfers the discriminability of the connective-augmented representations to implicit ones.