  Lexicalized parsing models are based on the assumptions that (i) constituents
are organized around a lexical head (ii) bilexical statistics are crucial to
solve ambiguities. In this paper, we introduce an unlexicalized
transition-based parser for discontinuous constituency structures, based on a
structure-label transition system and a bi-LSTM scoring system. We compare it
to lexicalized parsing models in order to address the question of
lexicalization in the context of discontinuous constituency parsing. Our
experiments show that unlexicalized models systematically achieve higher
results than lexicalized models, and provide additional empirical evidence that
lexicalization is not necessary to achieve strong parsing results. Our best
unlexicalized model sets a new state of the art on English and German
discontinuous constituency treebanks. We further provide a per-phenomenon
analysis of its errors on discontinuous constituents.
