  Correctly identifying multiword expressions (MWEs) is an important task for
most natural language processing systems since their misidentification can
result in ambiguity and misunderstanding of the underlying text. In this work,
we evaluate the performance of the mBERT model for MWE identification in a
multilingual context by training it on all 14 languages available in version
1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language
adversarial training into our methodology to create language-independent
embeddings and improve its capabilities in identifying multiword expressions.
The evaluation of our models shows that the approach employed in this work
achieves better results compared to the best system of the PARSEME 1.2
competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE
identification and on 12 out of 14 languages for unseen MWE identification.
Additionally, averaged across all languages, our best approach outperforms the
MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen
global MWE identification.
