When applied to classification tasks like language recognition, RNNs are typically combined with a "decoder": additional layer(s) that map their hidden states to a prediction.
despite differences in state expressiveness, rational RNNs might be able to achieve comparable empirical performance to non-rational RNNs on NLP tasks.