By the fact that rank(A + B)  rank(A) + rank(B), the ranks of P and Q are upper-bounded by r. As pointed out by Krueger and Memisevic (2017), the parameter matrices of a trained RNN tend to have full rank.
in the experiments, we set r equal to the hidden size d, aiming to allow the adaptive decoder to use full-rank matrices in the recurrent computation.