We compare the performance of an off-the-shelf MWE identification system based on neural sequence tagging <cite>(Zampieri et al., 2018)</cite> using lemmas and surface forms as input features, encoded in the form of classical pre-initialised word2vec embeddings (Mikolov et al., 2013) or, alternatively, using new-generation FastText embeddings built from character n-grams (Bojanowski et al., 2017) .