  There have been many proposals to reduce constituency parsing to tagging in
the literature. To better understand what these approaches have in common, we
cast several existing proposals into a unifying pipeline consisting of three
steps: linearization, learning, and decoding. In particular, we show how to
reduce tetratagging, a state-of-the-art constituency tagger, to shift--reduce
parsing by performing a right-corner transformation on the grammar and making a
specific independence assumption. Furthermore, we empirically evaluate our
taxonomy of tagging pipelines with different choices of linearizers, learners,
and decoders. Based on the results in English and a set of 8 typologically
diverse languages, we conclude that the linearization of the derivation tree
and its alignment with the input sequence is the most critical factor in
achieving accurate taggers.

Computation and Language