  Different linearizations have been proposed to cast dependency parsing as
sequence labeling and solve the task as: (i) a head selection problem, (ii)
finding a representation of the token arcs as bracket strings, or (iii)
associating partial transition sequences of a transition-based parser to words.
Yet, there is little understanding about how these linearizations behave in
low-resource setups. Here, we first study their data efficiency, simulating
data-restricted setups from a diverse set of rich-resource treebanks. Second,
we test whether such differences manifest in truly low-resource setups. The
results show that head selection encodings are more data-efficient and perform
better in an ideal (gold) framework, but that such advantage greatly vanishes
in favour of bracketing formats when the running setup resembles a real-world
low-resource configuration.
