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This paper presents a language-independent deep learning architecture adaptedto the task of multiword expression (MWE) identification. We employ a neuralarchitecture comprising of convolutional and recurrent layers with the additionof an optional CRF layer at the top. This system participated in the open trackof the Parseme shared task on automatic identification of verbal MWEs due tothe use of pre-trained wikipedia word embeddings. It outperformed allparticipating systems in both open and closed tracks with the overallmacro-average MWE-based F1 score of 58.09 averaged among all languages. Aparticular strength of the system is its superior performance on unseen dataentries.

SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation