  We use parsing as sequence labeling as a common framework to learn across
constituency and dependency syntactic abstractions. To do so, we cast the
problem as multitask learning (MTL). First, we show that adding a parsing
paradigm as an auxiliary loss consistently improves the performance on the
other paradigm. Secondly, we explore an MTL sequence labeling model that parses
both representations, at almost no cost in terms of performance and speed. The
results across the board show that on average MTL models with auxiliary losses
for constituency parsing outperform single-task ones by 1.14 F1 points, and for
dependency parsing by 0.62 UAS points.
