  Recent approaches to word sense disambiguation (WSD) utilize encodings of the
sense gloss (definition), in addition to the input context, to improve
performance. In this work we demonstrate that this approach can be adapted for
use in multiword expression (MWE) identification by training models which use
gloss and context information to filter MWE candidates produced by a rule-based
extraction pipeline. Our approach substantially improves precision,
outperforming the state-of-the-art in MWE identification on the DiMSUM dataset
by up to 1.9 F1 points and achieving competitive results on the PARSEME 1.1
English dataset. Our models also retain most of their WSD performance, showing
that a single model can be used for both tasks. Finally, building on similar
approaches using Bi-encoders for WSD, we introduce a novel Poly-encoder
architecture which improves MWE identification performance.
