  While being an essential component of spoken language, fillers (e.g."um" or
"uh") often remain overlooked in Spoken Language Understanding (SLU) tasks. We
explore the possibility of representing them with deep contextualised
embeddings, showing improvements on modelling spoken language and two
downstream tasks - predicting a speaker's stance and expressed confidence.
