  Transition-based top-down parsing with pointer networks has achieved
state-of-the-art results in multiple parsing tasks, while having a linear time
complexity. However, the decoder of these parsers has a sequential structure,
which does not yield the most appropriate inductive bias for deriving tree
structures. In this paper, we propose hierarchical pointer network parsers, and
apply them to dependency and sentence-level discourse parsing tasks. Our
results on standard benchmark datasets demonstrate the effectiveness of our
approach, outperforming existing methods and setting a new state-of-the-art.
