  We introduce a new method to tag Multiword Expressions (MWEs) using a
linguistically interpretable language-independent deep learning architecture.
We specifically target discontinuity, an under-explored aspect that poses a
significant challenge to computational treatment of MWEs. Two neural
architectures are explored: Graph Convolutional Network (GCN) and multi-head
self-attention. GCN leverages dependency parse information, and self-attention
attends to long-range relations. We finally propose a combined model that
integrates complementary information from both through a gating mechanism. The
experiments on a standard multilingual dataset for verbal MWEs show that our
model outperforms the baselines not only in the case of discontinuous MWEs but
also in overall F-score.
