Abstract
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 singletask ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points.