If the endocentric hypothesis is true (that is, the representation of a phrase is built from within depending on its components but independent of explicit category labels), then the nonterminal types should be easily inferred given the endocentrically-composed representation, and that ablating the nonterminal information would not make much difference in performance.
we train a GA-RNNG on unlabeled trees (only bracketings without nonterminal types), denoted U-GA-RNNG.