  We describe a method for developing broad-coverage semantic dependency
parsers for languages for which no semantically annotated resource is
available. We leverage a multitask learning framework coupled with an
annotation projection method. We transfer supervised semantic dependency parse
annotations from a rich-resource language to a low-resource language through
parallel data, and train a semantic parser on projected data. We make use of
supervised syntactic parsing as an auxiliary task in a multitask learning
framework, and show that with different multitask learning settings, we
consistently improve over the single-task baseline. In the setting in which
English is the source, and Czech is the target language, our best multitask
model improves the labeled F1 score over the single-task baseline by 1.8 in the
in-domain SemEval data (Oepen et al., 2015), as well as 2.5 in the
out-of-domain test set. Moreover, we observe that syntactic and semantic
dependency direction match is an important factor in improving the results.
