  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.
