  AI assistants can now carry out tasks for users by directly interacting with
website UIs. Current semantic parsing and slot-filling techniques cannot
flexibly adapt to many different websites without being constantly re-trained.
We propose FLIN, a natural language interface for web navigation that maps user
commands to concept-level actions (rather than low-level UI actions), thus
being able to flexibly adapt to different websites and handle their transient
nature. We frame this as a ranking problem: given a user command and a webpage,
FLIN learns to score the most relevant navigation instruction (involving action
and parameter values). To train and evaluate FLIN, we collect a dataset using
nine popular websites from three domains. Our results show that FLIN was able
to adapt to new websites in a given domain.
