  One of the most complex syntactic representations used in computational
linguistics and NLP are discontinuous constituent trees, crucial for
representing all grammatical phenomena of languages such as German. Recent
advances in dependency parsing have shown that Pointer Networks excel in
efficiently parsing syntactic relations between words in a sentence. This kind
of sequence-to-sequence models achieve outstanding accuracies in building
non-projective dependency trees, but its potential has not been proved yet on a
more difficult task. We propose a novel neural network architecture that, by
means of Pointer Networks, is able to generate the most accurate discontinuous
constituent representations to date, even without the need of Part-of-Speech
tagging information. To do so, we internally model discontinuous constituent
structures as augmented non-projective dependency structures. The proposed
approach achieves state-of-the-art results on the two widely-used NEGRA and
TIGER benchmarks, outperforming previous work by a wide margin.
