As we explained in the introduction, word embeddings are limited by meaning conflation around word types, and reduce NLM to fixed representations that are insensitive to contexts.
with fastText (Bojanowski et al., 2017) we're not restricted to a finite set of representations and can compositionally derive representations for word types unseen during training.