  In order to achieve deep natural language understanding, syntactic
constituent parsing is a vital step, highly demanded by many artificial
intelligence systems to process both text and speech. One of the most recent
proposals is the use of standard sequence-to-sequence models to perform
constituent parsing as a machine translation task, instead of applying
task-specific parsers. While they show a competitive performance, these
text-to-parse transducers are still lagging behind classic techniques in terms
of accuracy, coverage and speed. To close the gap, we here extend the framework
of sequence-to-sequence models for constituent parsing, not only by providing a
more powerful neural architecture for improving their performance, but also by
enlarging their coverage to handle the most complex syntactic phenomena:
discontinuous structures. To that end, we design several novel linearizations
that can fully produce discontinuities and, for the first time, we test a
sequence-to-sequence model on the main discontinuous benchmarks, obtaining
competitive results on par with task-specific discontinuous constituent parsers
and achieving state-of-the-art scores on the (discontinuous) English Penn
Treebank.
