  This paper presents a language-independent deep learning architecture adapted
to the task of multiword expression (MWE) identification. We employ a neural
architecture comprising of convolutional and recurrent layers with the addition
of an optional CRF layer at the top. This system participated in the open track
of the Parseme shared task on automatic identification of verbal MWEs due to
the use of pre-trained wikipedia word embeddings. It outperformed all
participating systems in both open and closed tracks with the overall
macro-average MWE-based F1 score of 58.09 averaged among all languages. A
particular strength of the system is its superior performance on unseen data
entries.
